MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Risk Prediction Models for Atherosclerotic Cardiovascular Disease in Patients with Chronic Kidney Disease: The CRIC Study.

BACKGROUND: Individuals with CKD may be at high risk for atherosclerotic cardiovascular disease (ASCVD). However, there are no ASCVD risk prediction models developed in CKD populations to inform clinical care and prevention. METHODS: We developed and validated 10-year ASCVD risk prediction models in patients with CKD that included participants without self-reported cardiovascular disease from the Chronic Renal Insufficiency Cohort (CRIC) study. ASCVD was defined as the first occurrence of adjudicated fatal and nonfatal stroke or myocardial infarction. Our models used clinically available variables and novel biomarkers. Model performance was evaluated based on discrimination, calibration, and net reclassification improvement. RESULTS: Of 2604 participants (mean age 55.8 years; 52.0% male) included in the analyses, 252 had incident ASCVD within 10 years of baseline. Compared with the American College of Cardiology/American Heart Association pooled cohort equations (area under the receiver operating characteristic curve [AUC]=0.730), a model with coefficients estimated within the CRIC sample had higher discrimination (P=0.03), achieving an AUC of 0.736 (95% confidence interval [CI], 0.649 to 0.826). The CRIC model developed using clinically available variables had an AUC of 0.760 (95% CI, 0.678 to 0.851). The CRIC biomarker-enriched model had an AUC of 0.771 (95% CI, 0.674 to 0.853), which was significantly higher than the clinical model (P=0.001). Both the clinical and biomarker-enriched models were well-calibrated and improved reclassification of nonevents compared with the pooled cohort equations (6.6%; 95% CI, 3.7% to 9.6% and 10.0%; 95% CI, 6.8% to 13.3%, respectively). CONCLUSIONS: The 10-year ASCVD risk prediction models developed in patients with CKD, including novel kidney and cardiac biomarkers, performed better than equations developed for the general population using only traditional risk factors.

Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multicohort Analysis.

BACKGROUND: Heart failure (HF) risk and the underlying risk factors vary by race. Traditional models for HF risk prediction treat race as a covariate in risk prediction and do not account for significant parameters such as cardiac biomarkers. Machine learning (ML) may offer advantages over traditional modeling techniques to develop race-specific HF risk prediction models and to elucidate important contributors of HF development across races. METHODS: We performed a retrospective analysis of 4 large, community cohort studies (ARIC [Atherosclerosis Risk in Communities], DHS [Dallas Heart Study], JHS [Jackson Heart Study], and MESA [Multi-Ethnic Study of Atherosclerosis]) with adjudicated HF events. The study included participants who were >40 years of age and free of HF at baseline. Race-specific ML models for HF risk prediction were developed in the JHS cohort (for Black race-specific model) and White adults from ARIC (for White race-specific model). The models included 39 candidate variables across demographic, anthropometric, medical history, laboratory, and electrocardiographic domains. The ML models were externally validated and compared with prior established traditional and non-race-specific ML models in race-specific subgroups of the pooled MESA/DHS cohort and Black participants of ARIC. The Harrell C-index and Greenwood-Nam-D'Agostino χ2 tests were used to assess discrimination and calibration, respectively. RESULTS: The ML models had excellent discrimination in the derivation cohorts for Black (n=4141 in JHS, C-index=0.88) and White (n=7858 in ARIC, C-index=0.89) participants. In the external validation cohorts, the race-specific ML model demonstrated adequate calibration and superior discrimination (Black individuals, C-index=0.80-0.83; White individuals, C-index=0.82) compared with established HF risk models or with non-race-specific ML models derived with race included as a covariate. Among the risk factors, natriuretic peptide levels were the most important predictor of HF risk across both races, followed by troponin levels in Black and ECG-based Cornell voltage in White individuals. Other key predictors of HF risk among Black individuals were glycemic parameters and socioeconomic factors. In contrast, prevalent cardiovascular disease and traditional cardiovascular risk factors were stronger predictors of HF risk in White adults. CONCLUSIONS: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance compared with traditional HF risk and non-race-specific ML models. This approach identifies distinct race-specific contributors of HF.

Inflammation biomarkers and incident coronary heart disease: the Reasons for Geographic And Racial Differences in Stroke Study.

BACKGROUND: Individual inflammation biomarkers are associated with incident coronary heart disease (CHD) events. However, there is limited research on whether the risk for incident CHD is progressively higher with a higher number of inflammation biomarkers in abnormal levels. METHODS: We used data from 15,758 Reasons for Geographic and Racial Differences in Stroke (REGARDS) study participants aged ≥45 years without a history of CHD at baseline in 2003-2007. Abnormal levels of baseline high-sensitivity C-reactive protein, leukocyte count and serum albumin were defined as ≥3.8 mg/L (3rd tertile), ≥6.3 x 109 cells/L (3rd tertile), and <4.0 g/dL (1st tertile), respectively. The outcome was a composite of incident myocardial infarction or CHD death. RESULTS: Overall, 38.9% (n = 6,123) had 0, 36.6% (n = 5,774) had 1, 19.8% (n = 3,113) had 2 and 4.7% (n = 748) had 3 biomarkers of inflammation in abnormal levels. Over a median follow-up of 11.4 years, 954 (6.1%) participants had incident CHD. The rate of incident CHD per 1000 person-years for individuals with 0, 1, 2, and 3 biomarkers of inflammation in abnormal levels was 4.4 (95% confidence interval [CI]: 3.9-5.0), 6.3 (95% CI: 5.6-6.9), 8.8 (95% CI: 7.8-9.9), and 10.6 (95% CI: 8.1-13.1), respectively. Multi-variable adjusted hazard ratios for incident CHD associated with 1, 2 and 3 versus no inflammation biomarker in abnormal levels were 1.26 (95% CI: 1.07-1.49), 1.72 (95% CI: 1.43-2.07), and 1.84 (95% CI: 1.37-2.47), respectively (P-trend < .001). CONCLUSIONS: The number of inflammation markers in abnormal levels was associated with increased risk of incident CHD after multi-variable adjustment.

Spike-and-slab least absolute shrinkage and selection operator generalized additive models and scalable algorithms for high-dimensional data analysis.

There are proposals that extend the classical generalized additive models (GAMs) to accommodate high-dimensional data ( p ≫ n $$ p\gg n $$ ) using group sparse regularization. However, the sparse regularization may induce excess shrinkage when estimating smooth functions, damaging predictive performance. Moreover, most of these GAMs consider an "all-in-all-out" approach for functional selection, rendering them difficult to answer if nonlinear effects are necessary. While some Bayesian models can address these shortcomings, using Markov chain Monte Carlo algorithms for model fitting creates a new challenge, scalability. Hence, we propose Bayesian hierarchical generalized additive models as a solution: we consider the smoothing penalty for proper shrinkage of curve interpolation via reparameterization. A novel two-part spike-and-slab LASSO prior for smooth functions is developed to address the sparsity of signals while providing extra flexibility to select the linear or nonlinear components of smooth functions. A scalable and deterministic algorithm, EM-Coordinate Descent, is implemented in an open-source R package BHAM. Simulation studies and metabolomics data analyses demonstrate improved predictive and computational performance against state-of-the-art models. Functional selection performance suggests trade-offs exist regarding the effect hierarchy assumption.

Development and validation of optimal phenomapping methods to estimate long-term atherosclerotic cardiovascular disease risk in patients with type 2 diabetes.

AIMS/HYPOTHESIS: Type 2 diabetes is a heterogeneous disease process with variable trajectories of CVD risk. We aimed to evaluate four phenomapping strategies and their ability to stratify CVD risk in individuals with type 2 diabetes and to identify subgroups who may benefit from specific therapies. METHODS: Participants with type 2 diabetes and free of baseline CVD in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial were included in this study (N = 6466). Clustering using Gaussian mixture models, latent class analysis, finite mixture models (FMMs) and principal component analysis was compared. Clustering variables included demographics, medical and social history, laboratory values and diabetes complications. The interaction between the phenogroup and intensive glycaemic, combination lipid and intensive BP therapy for the risk of the primary outcome (composite of fatal myocardial infarction, non-fatal myocardial infarction or unstable angina) was evaluated using adjusted Cox models. The phenomapping strategies were independently assessed in an external validation cohort (Look Action for Health in Diabetes [Look AHEAD] trial: n = 4211; and Bypass Angioplasty Revascularisation Investigation 2 Diabetes [BARI 2D] trial: n = 1495). RESULTS: Over 9.1 years of follow-up, 789 (12.2%) participants had a primary outcome event. FMM phenomapping with three phenogroups was the best-performing clustering strategy in both the derivation and validation cohorts as determined by Bayesian information criterion, Dunn index and improvement in model discrimination. Phenogroup 1 (n = 663, 10.3%) had the highest burden of comorbidities and diabetes complications, phenogroup 2 (n = 2388, 36.9%) had an intermediate comorbidity burden and lowest diabetes complications, and phenogroup 3 (n = 3415, 52.8%) had the fewest comorbidities and intermediate burden of diabetes complications. Significant interactions were observed between phenogroups and treatment interventions including intensive glycaemic control (p-interaction = 0.042) and combination lipid therapy (p-interaction < 0.001) in the ACCORD, intensive lifestyle intervention (p-interaction = 0.002) in the Look AHEAD and early coronary revascularisation (p-interaction = 0.003) in the BARI 2D trial cohorts for the risk of the primary composite outcome. Favourable reduction in the risk of the primary composite outcome with these interventions was noted in low-risk participants of phenogroup 3 but not in other phenogroups. Compared with phenogroup 3, phenogroup 1 participants were more likely to have severe/symptomatic hypoglycaemic events and medication non-adherence on follow-up in the ACCORD and Look AHEAD trial cohorts. CONCLUSIONS/INTERPRETATION: Clustering using FMMs was the optimal phenomapping strategy to identify replicable subgroups of patients with type 2 diabetes with distinct clinical characteristics, CVD risk and response to therapies.

Racial Differences in Blood Pressure Control Following Stroke: The REGARDS Study.

BACKGROUND AND PURPOSE: In the general population, Black adults are less likely than White adults to have controlled blood pressure (BP), and when not controlled, they are at greater risk for stroke compared with White adults. High BP is a major modifiable risk factor for recurrent stroke, but few studies have examined racial differences in BP control among stroke survivors. METHODS: We used data from the REGARDS study (Reasons for Geographic and Racial Differences in Stroke) to examine disparities in BP control between Black and White adults, with and without a history of stroke. We studied participants taking antihypertensive medication who did and did not experience an adjudicated stroke (n=306 and 7693 participants, respectively) between baseline (2003-2007) and a second study visit (2013-2016). BP control at the second study visit was defined as systolic BP <130 mm Hg and diastolic BP <80 mm Hg except for low-risk adults ≥65 years of age (ie, those without diabetes, chronic kidney disease, history of cardiovascular disease, and with a 10-year predicted atherosclerotic cardiovascular disease risk <10%) for whom BP control was defined as systolic BP <130 mm Hg. RESULTS: Among participants with a history of stroke, 50.3% of White compared with 39.3% of Black participants had controlled BP. Among participants without a history of stroke, 56.0% of White compared with 50.2% of Black participants had controlled BP. After multivariable adjustment, there was a tendency for Black participants to be less likely than White participants to have controlled BP (prevalence ratio, 0.77 [95% CI, 0.59-1.02] for those with a history of stroke and 0.92 [95% CI, 0.88-0.97] for those without a history of stroke). CONCLUSIONS: There was a lower proportion of controlled BP among Black compared with White adults with or without stroke, with no statistically significant differences after multivariable adjustment.

Factors associated with breast cancer screening behaviors in a sample of Jamaican women in 2013.

In this study authors aimed to investigate the barriers to participation of a sample of Jamaican women in mammography screening. The data were obtained from a previous survey that was conducted in Jamaica from June to August 2013 in the four parishes (Hanover, St. James, Trelawny, and Westmoreland) served by the Western Regional Health Authority (WRHA). WRHA served four hospitals, five non-randomized health centers, and five sites of community events. The sample consisted of women ranging in age between 35 and 83 years, with the mean age of 50.2 (SD = 10.6). The authors used a logistic regression to determine the extent to which variables were associated with breast cancer screening. The level of significace was 0.1 for the bivariate and multivariable analysis. The main factors associated with the lack of breast cancer screening among the women were: being less than 50 years old, single, never thought about breast cancer screening, in need of childcare, and having more than three children. We identified significant barriers to participation in mammography screening experienced by a sample of Jamaican women. Our results suggest that it is necessary to increase the awareness of the importance of breast screening importance among the women who may never have thought about mammography.

Trends in Blood Pressure Control Among US Adults With Hypertension, 1999-2000 to 2017-2018.

Importance: Controlling blood pressure (BP) reduces the risk for cardiovascular disease. Objective: To determine whether BP control among US adults with hypertension changed from 1999-2000 through 2017-2018. Design, Setting, and Participants: Serial cross-sectional analysis of National Health and Nutrition Examination Survey data, weighted to be representative of US adults, between 1999-2000 and 2017-2018 (10 cycles), including 18 262 US adults aged 18 years or older with hypertension defined as systolic BP level of 140 mm Hg or higher, diastolic BP level of 90 mm Hg or higher, or use of antihypertensive medication. The date of final data collection was 2018. Exposures: Calendar year. Main Outcomes and Measures: Mean BP was computed using 3 measurements. The primary outcome of BP control was defined as systolic BP level lower than 140 mm Hg and diastolic BP level lower than 90 mm Hg. Results: Among the 51 761 participants included in this analysis, the mean (SD) age was 48 (19) years and 25 939 (50.1%) were women; 43.2% were non-Hispanic White adults; 21.6%, non-Hispanic Black adults; 5.3%, non-Hispanic Asian adults; and 26.1%, Hispanic adults. Among the 18 262 adults with hypertension, the age-adjusted estimated proportion with controlled BP increased from 31.8% (95% CI, 26.9%-36.7%) in 1999-2000 to 48.5% (95% CI, 45.5%-51.5%) in 2007-2008 (P < .001 for trend), remained stable and was 53.8% (95% CI, 48.7%-59.0%) in 2013-2014 (P = .14 for trend), and then declined to 43.7% (95% CI, 40.2%-47.2%) in 2017-2018 (P = .003 for trend). Compared with adults who were aged 18 years to 44 years, it was estimated that controlled BP was more likely among those aged 45 years to 64 years (49.7% vs 36.7%; multivariable-adjusted prevalence ratio, 1.18 [95% CI, 1.02-1.37]) and less likely among those aged 75 years or older (37.3% vs 36.7%; multivariable-adjusted prevalence ratio, 0.81 [95% CI, 0.65-0.97]). It was estimated that controlled BP was less likely among non-Hispanic Black adults vs non-Hispanic White adults (41.5% vs 48.2%, respectively; multivariable-adjusted prevalence ratio, 0.88; 95% CI, 0.81-0.96). Controlled BP was more likely among those with private insurance (48.2%), Medicare (53.4%), or government health insurance other than Medicare or Medicaid (43.2%) vs among those without health insurance (24.2%) (multivariable-adjusted prevalence ratio, 1.40 [95% CI, 1.08-1.80], 1.47 [95% CI, 1.15-1.89], and 1.36 [95% CI, 1.04-1.76], respectively). Controlled BP was more likely among those with vs those without a usual health care facility (48.4% vs 26.5%, respectively; multivariable-adjusted prevalence ratio, 1.48 [95% CI, 1.13-1.94]) and among those who had vs those who had not had a health care visit in the past year (49.1% vs 8.0%; multivariable-adjusted prevalence ratio, 5.23 [95% CI, 2.88-9.49]). Conclusions and Relevance: In a series of cross-sectional surveys weighted to be representative of the adult US population, the prevalence of controlled BP increased between 1999-2000 and 2007-2008, did not significantly change from 2007-2008 through 2013-2014, and then decreased after 2013-2014.

Prevalence of Activity Limitations and Association with Multimorbidity Among US Adults 50 to 64 Years Old.

BACKGROUND: Functional limitations may be more common in middle-aged adults than previously recognized. However, there are few published data on the prevalence of activity limitations, and their association with multimorbidity, among adults 50 to 64 years old. OBJECTIVE: To describe the prevalence of activity limitations and the association with multimorbidity in middle-aged adults. DESIGN: Cross-sectional analysis of US population-based National Health and Nutrition Examination Survey (NHANES) 2011-2016. PARTICIPANTS: The total number of community-dwelling NHANES participants aged 50-64 years old is 4217. MAIN MEASURES: Chronic conditions included hypertension, high cholesterol, diabetes mellitus, obesity, chronic kidney disease, cancer, stroke, coronary heart disease, heart failure, chronic obstructive pulmonary disease/asthma, arthritis, and depression. Activity limitations were defined as any difficulty within each of four International Classification of Functioning (ICF) domains: functional limitations (kneeling, carrying, standing, sitting, reaching, grasping, pulling), mobility (walking » mile, climbing 10 steps), basic activities of daily living (BADLs; walking, transferring, eating, dressing), and instrumental activities of daily living (IADLs; finances, chores, cooking). We calculated prevalence ratios for activity limitations using generalized estimating equations. KEY RESULTS: The prevalence of functional limitations, mobility limitations, BADL difficulty, and IADL difficulty was 34%, 11%, 15%, and 17%, respectively. Seventy-two percent of participants had two or more chronic conditions; 23% had two, 18% had three, 15% had four, and 16% had five or more. Multivariable adjusted prevalence ratios (95% CI) for functional limitations among those with 2, 3, 4, and 5 or more chronic conditions, compared with 0-1 conditions, were 1.94 (1.43-2.63), 2.50 (1.93-3.23), 3.26 (2.48-4.27), and 4.54 (3.48-5.93), respectively (p trend < 0.001). Larger prevalence ratios at a higher number of chronic conditions were present for mobility limitations, BADL difficulty, and IADL difficulty. CONCLUSIONS: Problems with function are not limited to older adults and multimorbidity may be helpful for identifying middle-aged adults with a high prevalence of activity limitations.

Assessing responsiveness over time of the PROMIS® pediatric symptom and function measures in cancer, nephrotic syndrome, and sickle cell disease.

PURPOSE: Previous studies provided evidence for the validity of the PROMIS Pediatric measures in cross-sectional studies. This study evaluated the ability of the PROMIS Pediatric measures to detect change over time in children and adolescents with cancer, nephrotic syndrome (NS), or sickle cell disease (SCD). METHODS: Participants (8-17 years) completed measures of fatigue, pain interference, anger, anxiety, depressive symptoms, mobility, upper extremity, and peer relationships at three or four time points (T1-T4). Between T1 and T2, children with cancer received chemotherapy and children with SCD experienced a pain exacerbation. Children with NS were first assessed during active disease (T2), with T3 and T4 conducted at disease remission. For the primary analysis of responsiveness, we expected better scores at T3 (recovery) compared to T2 (event) for all diseases. T1 and T4 are also expected to have better scores than T2. Linear mixed models were used and adjusted for time, gender, age, race/ethnicity, education, comorbid conditions, and disease. RESULTS: Enrolled were 96 children with cancer, 121 children with SCD, and 127 children with NS. Fatigue, pain interference, mobility, and upper extremity scores worsened from T1 (baseline) to T2 (event) (p < 0.01), and significantly improved from T2 to T3 and T4 (p < 0.01). Similarly, anxiety and depressive symptoms significantly improved from T2 to T3 and T4 (p < 0.01). CONCLUSIONS: This study provides evidence for the responsiveness of seven PROMIS Pediatric measures to clinical disease state in three chronic illnesses. The findings support use of PROMIS Pediatric measures in clinical research.

Social Determinants of Health and Incident Apparent Treatment-Resistant Hypertension Among White and Black US Adults: The REGARDS Study.

BACKGROUND: We examined the association of multilevel social determinants of health with incident apparent treatment-resistant hypertension (aTRH). METHODS AND RESULTS: We analyzed data from 2774 White and 2257 Black US adults from the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study taking antihypertensive medication without aTRH at baseline to estimate the association of social determinants of health with incident aTRH. Selection of social determinants of health was guided by the Healthy People 2030 domains of education, economic stability, social context, neighborhood environment, and health care access. Blood pressure (BP) was measured during study visits, and antihypertensive medication classes were identified through a pill bottle review. Incident aTRH was defined as (1) systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg, or systolic BP ≥130 mm Hg or diastolic BP ≥80 mm Hg for those with diabetes or chronic kidney disease while taking ≥3 classes of antihypertensive medication or (2) taking ≥4 classes of antihypertensive medication regardless of BP level, at the follow-up visit. Over a median 9.5 years of follow-up, 15.9% of White and 24.0% of Black adults developed aTRH. A percent of the excess aTRH risk among Black versus White adults was mediated by low education (14.2%), low income (16.0%), not seeing a friend or relative in the past month (8.1%), not having someone to care for them if ill or disabled (7.6%), lack of health insurance (10.6%), living in a disadvantaged neighborhood (18.0%), and living in states with poor public health infrastructure (6.0%). CONCLUSIONS: Part of the association between race and incident aTRH risk was mediated by social determinants of health.

Five-Year Cumulative Cardiovascular Health and Clinical Events in Patients With Chronic Kidney Disease: The CRIC Study.

BACKGROUND: Higher cardiovascular health (CVH) score is associated with lower risks of cardiovascular disease (CVD) and mortality in the general population. However, it is unclear whether cumulative CVH is associated with CVD, end-stage kidney disease (ESKD), and death in patients with chronic kidney disease. METHODS AND RESULTS: Among individuals from the prospective CRIC (Chronic Renal Insufficiency Cohort) Study, we used the percentage of the maximum possible CVH score attained from baseline to the year 5 visit to calculate cumulative CVH score. Multivariable-adjusted Cox proportional hazards regression was used to investigate the associations of cumulative CVH with risks of adjudicated CVD (myocardial infarction, stroke, and heart failure), ESKD, and all-cause mortality. A total of 3939 participants (mean age, 57.7 years; 54.9% men) were included. The mean (SD) cumulative CVH score attained during 5 years was 55.5% (12.3%). Over a subsequent median 10.2-year follow-up, 597 participants developed CVD, 656 had ESKD, and 1324 died. A higher cumulative CVH score was significantly associated with lower risks of CVD, ESKD, and mortality, independent of the CVH score at year 5. Multivariable-adjusted hazard ratios and 95% CIs per 10% higher cumulative CVH score during 5 years were 0.81 (0.69-0.95) for CVD, 0.82 (0.70-0.97) for ESKD, and 0.80 (0.72-0.89) for mortality. CONCLUSIONS: Among patients with chronic kidney disease stages 2 to 4, a better CVH status maintained throughout 5 years is associated with lower risks of CVD, ESKD, and all-cause mortality. The findings support the need for interventions to maintain ideal CVH status for prevention of adverse outcomes in the population with chronic kidney disease.

Race differences in ambulatory blood pressure monitoring parameters.

BACKGROUND: Mean systolic and diastolic blood pressure (SBP and DBP) on ambulatory blood pressure (BP) monitoring (ABPM) are higher among Black compared with White adults. With 48 to 72 BP measurements obtained over 24 h, ABPM can generate parameters other than mean BP that are associated with increased risk for cardiovascular events. There are few data on race differences in ABPM parameters other than mean BP. METHODS: To estimate differences between White and Black participants in ABPM parameters, we used pooled data from five US-based studies in which participants completed ABPM (n = 2580). We calculated measures of SBP and DBP level, including mean, load, peak, and measures of SBP and DBP variability, including average real variability (ARV) and peak increase. RESULTS: There were 1513 (58.6%) Black and 1067 (41.4%) White participants with mean ages of 56.1 and 49.0 years, respectively. After multivariable adjustment, asleep SBP and DBP load were 5.7% (95% CI: 3.5-7.9%) and 2.7% (95% CI: 1.1-4.3%) higher, respectively, among Black compared with White participants. Black compared with White participants also had higher awake DBP ARV (0.3 [95%CI: 0.0-0.6] mmHg) and peak increase in DBP (0.4 [95% CI: 0.0-0.8] mmHg). There was no evidence of Black:White differences in awake measures of SBP level, asleep peak SBP or DBP, awake and asleep measures of SBP variability or asleep measures of DBP variability after multivariable adjustment. CONCLUSION: Asleep SBP load, awake DBP ARV and peak increase in awake DBP were higher in Black compared to White participants, independent of mean BP on ABPM.

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.

Social Determinants of Health and Uncontrolled Blood Pressure in a National Cohort of Black and White US Adults: the REGARDS Study.

BACKGROUND: Determining the contribution of social determinants of health (SDOH) to the higher proportion of Black adults with uncontrolled blood pressure (BP) could inform interventions to improve BP control and reduce cardiovascular disease. METHODS: We analyzed data from 7306 White and 7497 Black US adults taking antihypertensive medication from the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study (2003-2007). SDOH were defined using the Healthy People 2030 domains of education, economic stability, social context, neighborhood environment, and health care access. Uncontrolled BP was defined as systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg. RESULTS: Among participants taking antihypertensive medication, 25.4% of White and 33.7% of Black participants had uncontrolled BP. The SDOH included in the current analysis mediated the Black-White difference in uncontrolled BP by 33.0% (95% CI, 22.1%-46.8%). SDOH that contributed to excess uncontrolled BP among Black compared with White adults included low annual household income (percent-mediated 15.8% [95% CI, 10.8%-22.8%]), low education (10.5% [5.6%-15.4%]), living in a health professional shortage area (10.4% [6.5%-14.7%]), disadvantaged neighborhood (11.0% [4.4%-18.0%]), and high-poverty zip code (9.7% [3.8%-15.5%]). Together, the neighborhood-domain accounted for 14.1% (95% CI, 5.9%-22.9%), the health care domain accounted for 12.7% (95% CI, 8.4%-17.3%), and the social-context-domain accounted for 3.8% (95% CI, 1.2%-6.6%) of the excess likelihood of uncontrolled BP among Black compared with White adults, respectively. CONCLUSIONS: SDOH including low education, low income, living in a health professional shortage area, disadvantaged neighborhood, and high-poverty zip code contributed to the excess likelihood of uncontrolled BP among Black compared with White adults.

Hypertension Statistics for US Adults: An Open-Source Web Application for Analysis and Visualization of National Health and Nutrition Examination Survey Data.

BACKGROUND: Data from the US National Health and Nutrition Examination Survey are freely available and can be analyzed to produce hypertension statistics for the noninstitutionalized US population. The analysis of these data requires statistical programming expertise and knowledge of National Health and Nutrition Examination Survey methodology. METHODS: We developed a web-based application that provides hypertension statistics for US adults using 10 cycles of National Health and Nutrition Examination Survey data, 1999 to 2000 through 2017 to 2020. We validated the application by reproducing results from prior publications. The application's interface allows users to estimate crude and age-adjusted means, quantiles, and proportions. Population counts can also be estimated. To demonstrate the application's capabilities, we estimated hypertension statistics for noninstitutionalized US adults. RESULTS: The estimated mean systolic blood pressure (BP) declined from 123 mm Hg in 1999 to 2000 to 120 mm Hg in 2009 to 2010 and increased to 123 mm Hg in 2017 to 2020. The age-adjusted prevalence of hypertension (ie, systolic BP≥130 mm Hg, diastolic BP≥80 mm Hg or self-reported antihypertensive medication use) was 47.9% in 1999 to 2000, 43.0% in 2009 to 2010, and 44.7% in 2017 to 2020. In 2017 to 2020, an estimated 115.3 million US adults had hypertension. The age-adjusted prevalence of controlled BP, defined by the 2017 American College of Cardiology/American Heart Association BP guideline, among nonpregnant US adults with hypertension was 9.7% in 1999 to 2000, 25.0% in 2013 to 2014, and 21.9% in 2017 to 2020. After age adjustment and among nonpregnant US adults who self-reported taking antihypertensive medication, 27.5%, 48.5%, and 43.0% had controlled BP in 1999 to 2000, 2013 to 2014, and 2017 to 2020, respectively. CONCLUSIONS: The application developed in the current study is publicly available at https://bcjaeger.shinyapps.io/nhanesShinyBP/ and produced valid, transparent and reproducible results.

Analysis of Cardiorespiratory Fitness in Early Adulthood and Midlife With All-Cause Mortality and Fatal or Nonfatal Cardiovascular Disease.

Importance: Optimizing cardiovascular fitness is a prevention strategy against premature death and cardiovascular disease (CVD) events. Since this evidence has largely been established in older populations, the importance of cardiorespiratory fitness during earlier periods of adulthood remains unclear. Objective: To examine the association of early-adulthood cardiorespiratory fitness and percentage of early-adulthood cardiorespiratory fitness retained during midlife with subsequent risk of all-cause mortality and CVD-related morbidity and mortality overall as well as by sex and race. Design, Setting, and Participants: This retrospective population-based cohort study analyzed data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, an ongoing prospective cohort study conducted at field center clinics in Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; and Oakland, California. Participants in the CARDIA study were aged 18 to 30 years when they completed the baseline graded exercise test protocol in 1985 to 1986 and have since undergone follow-up examinations biannually and every 2 to 5 years. Data were collected through August 31, 2020, and were analyzed in October 2022. Exposures: Cardiorespiratory fitness was estimated from a symptom-limited, maximal graded exercise test protocol conducted at baseline and at year 7 and year 20 follow-up examinations. Main Outcomes and Measures: All-cause mortality and combined fatal and nonfatal CVD events were obtained since year 20 of follow-up examinations through August 31, 2020. Cox proportional hazards regression models were used to estimate hazard ratios (HRs) for each primary exposure with each outcome. Results: A total of 4808 participants (mean [SD] age at baseline, 24.8 [3.7] years; 2670 females [56%]; 2438 Black individuals [51%]) were included in the sample. During 68 751 person-years of follow-up, there were 302 deaths (6.3%) and 274 CVD events (5.7%) since year 20. Every 1-minute increment in cardiorespiratory fitness at baseline was associated with a lower risk of all-cause mortality in females (HR, 0.73; 95% CI, 0.64-0.82) and males (HR, 0.87; 95% CI, 0.80-0.96). Every 5% increment in cardiorespiratory fitness retained through year 20 was associated with a lower risk of all-cause mortality (HR, 0.89; 95% CI, 0.79-0.99), but no evidence of effect modification by race or sex was found. Every 1-minute increment in cardiorespiratory fitness at baseline was associated with a lower risk of fatal or nonfatal CVD (HR, 0.89; 95% CI, 0.82-0.96), and the estimated HR per 5% increment in cardiorespiratory fitness retained throughout midlife was 0.89 (95% CI, 0.78-1.00), with no evidence for interaction by race or sex. Conclusions and Relevance: This cohort study found that higher early-adulthood cardiorespiratory fitness and greater retention of early-adulthood cardiorespiratory fitness throughout midlife were associated with a lower risk of premature death and CVD events. Additional research is needed to clarify the association of cardiorespiratory fitness timing across the life course with risk of clinical outcomes.

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.