Life Course Cardiovascular Health: Risk Factors, Outcomes, and Interventions.

Since it was first defined by the American Heart Association in 2010, cardiovascular health (CVH) has been extensively studied across the life course. In this review, we present the current literature examining early life predictors of CVH, the later life outcomes of child CVH, and the relatively few interventions which have specifically addressed how to preserve and promote CVH across populations. We find that research on CVH has demonstrated that prenatal and childhood exposures are consistently associated with CVH trajectories from childhood through adulthood. CVH measured at any point in life is strongly predictive of future cardiovascular disease, dementia, cancer, and mortality as well as a variety of other health outcomes. This speaks to the importance of intervening early to prevent the loss of optimal CVH and the accumulation of cardiovascular risk. Interventions to improve CVH are not common but those that have been published most often address multiple modifiable risk factors among individuals within the community. Relatively few interventions have been focused on improving the construct of CVH in children. Future research is needed that will be both effective, scalable, and sustainable. Technology including digital platforms as well as implementation science will play key roles in achieving this vision. In addition, community engagement at all stages of this research is critical. Lastly, prevention strategies that are tailored to the individual and their context may help us achieve the promise of personalized prevention and help promote ideal CVH in childhood and across the life course.

Development and Validation of a Long-Term Incident Heart Failure Risk Model.

BACKGROUND: Average lifetime risk for heart failure (HF) is high but differs significantly across and within sex-race groups. No models for estimating long-term risk for HF exist, which would allow for earlier identification and interventions in high-risk subsets. The authors aim to derive 30-year HF risk equations. METHODS: Adults between the ages of 20 to 59 years and free of cardiovascular disease at baseline from 5 population-based cohorts were included. Among 24 838 participants (55% women, 25% Black based on self-report), follow-up consisted of 599 551 person-years. Sex- and race-specific 30-year HF risk equations were derived and validated accounting for competing risk of non-HF death. HF was based on a clinical diagnosis. Model discrimination and calibration were assessed using 10-fold cross-validation. Finally, the model was applied to varying risk factor patterns for systematic examination. RESULTS: The rate of incident HF was 4.0 per 1000 person-years. Harrell C statistics were 0.82 (0.80-0.83) and 0.84 (0.82-0.85) in White and Black men and 0.84 (0.82-0.85) and 0.85 (0.83-0.87) in White and Black women, respectively. Hosmer-Lemeshow calibration was acceptable, with χ2 <30 in all subgroups. Risk estimation varied across sex-race groups: for example, in an average 40-year-old nonsmoker with an untreated systolic blood pressure of 140 mm Hg and body mass index of 30 kg/m2, risk was estimated to be 22.8% in a Black man, 13.7% in a White man, 13.0% in a Black woman, and 12.1% in a White woman. CONCLUSIONS: Sex- and race-specific equations for prediction of long-term risk of HF demonstrated high discrimination and adequate calibration.

Longitudinal associations of screen time, physical activity, and sleep duration with body mass index in U.S. youth.

BACKGROUND: Youth use different forms of screen time (e.g., streaming, gaming) that may be related to body mass index (BMI). Screen time is non-independent from other behaviors, including physical activity and sleep duration. Statistical approaches such as isotemporal substitution or compositional data analysis (CoDA) can model associations between these non-independent behaviors and health outcomes. Few studies have examined different types of screen time, physical activity, and sleep duration simultaneously in relation to BMI. METHODS: Data were baseline (2017-2018) and one-year follow-up (2018-2019) from the Adolescent Brain Cognitive Development Study, a multi-site study of a nationally representative sample of U.S. youth (N = 10,544, mean [SE] baseline age = 9.9 [0.03] years, 48.9% female, 45.4% non-White). Participants reported daily minutes of screen time (streaming, gaming, socializing), physical activity, and sleep. Sex-stratified models estimated the association between baseline behaviors and follow-up BMI z-score, controlling for demographic characteristics, internalizing symptoms, and BMI z-score at baseline. RESULTS: In females, isotemporal substitution models estimated that replacing 30 min of socializing (ß [95% CI] = -0.03 [-0.05, -0.002]), streaming (-0.03 [-0.05, -0.01]), or gaming (-0.03 [-0.06, -0.01]) with 30 min of physical activity was associated with a lower follow-up BMI z-score. In males, replacing 30 min of socializing (-0.03 [-0.05, -0.01]), streaming (-0.02 [-0.03, -0.01]), or gaming (-0.02 [-0.03, -0.01]) with 30 min of sleep was associated with a lower follow-up BMI z-score. In males, replacing 30 min of socializing with 30 min of gaming was associated with a lower follow-up BMI z-score (-0.01 [-0.03, -0.0001]). CoDA estimated that in males, a greater proportion of time spent in baseline socializing, relative to the remaining behaviors, was associated with a higher follow-up BMI z-score (0.05 [0.02, 0.08]). In females, no associations between screen time and BMI were observed using CoDA. CONCLUSIONS: One-year longitudinal associations between screen time and BMI may depend on form of screen time, what behavior it replaces (physical activity or sleep), and participant sex. The alternative statistical approaches yielded somewhat different results. Experimental manipulation of screen time and investigation of biopsychosocial mechanisms underlying the observed sex differences will allow for causal inference and can inform interventions.

Dietary metabolic signatures and cardiometabolic risk.

AIMS: Observational studies of diet in cardiometabolic-cardiovascular disease (CM-CVD) focus on self-reported consumption of food or dietary pattern, with limited information on individual metabolic responses to dietary intake linked to CM-CVD. Here, machine learning approaches were used to identify individual metabolic patterns related to diet and relation to long-term CM-CVD in early adulthood. METHODS AND RESULTS: In 2259 White and Black adults (age 32.1 ± 3.6 years, 45% women, 44% Black) in the Coronary Artery Risk Development in Young Adults (CARDIA) study, multivariate models were employed to identify metabolite signatures of food group and composite dietary intake across 17 food groups, 2 nutrient groups, and healthy eating index-2015 (HEI2015) diet quality score. A broad array of metabolites associated with diet were uncovered, reflecting food-related components/catabolites (e.g. fish and long-chain unsaturated triacylglycerols), interactions with host features (microbiome), or pathways broadly implicated in CM-CVD (e.g. ceramide/sphingomyelin lipid metabolism). To integrate diet with metabolism, penalized machine learning models were used to define a metabolite signature linked to a putative CM-CVD-adverse diet (e.g. high in red/processed meat, refined grains), which was subsequently associated with long-term diabetes and CVD risk numerically more strongly than HEI2015 in CARDIA [e.g. diabetes: standardized hazard ratio (HR): 1.62, 95% confidence interval (CI): 1.32-1.97, P < 0.0001; CVD: HR: 1.55, 95% CI: 1.12-2.14, P = 0.008], with associations replicated for diabetes (P < 0.0001) in the Framingham Heart Study. CONCLUSION: Metabolic signatures of diet are associated with long-term CM-CVD independent of lifestyle and traditional risk factors. Metabolomics improves precision to identify adverse consequences and pathways of diet-related CM-CVD.

Life's Essential 8: Updating and Enhancing the American Heart Association's Construct of Cardiovascular Health: A Presidential Advisory From the American Heart Association.

In 2010, the American Heart Association defined a novel construct of cardiovascular health to promote a paradigm shift from a focus solely on disease treatment to one inclusive of positive health promotion and preservation across the life course in populations and individuals. Extensive subsequent evidence has provided insights into strengths and limitations of the original approach to defining and quantifying cardiovascular health. In response, the American Heart Association convened a writing group to recommend enhancements and updates. The definition and quantification of each of the original metrics (Life's Simple 7) were evaluated for responsiveness to interindividual variation and intraindividual change. New metrics were considered, and the age spectrum was expanded to include the entire life course. The foundational contexts of social determinants of health and psychological health were addressed as crucial factors in optimizing and preserving cardiovascular health. This presidential advisory introduces an enhanced approach to assessing cardiovascular health: Life's Essential 8. The components of Life's Essential 8 include diet (updated), physical activity, nicotine exposure (updated), sleep health (new), body mass index, blood lipids (updated), blood glucose (updated), and blood pressure. Each metric has a new scoring algorithm ranging from 0 to 100 points, allowing generation of a new composite cardiovascular health score (the unweighted average of all components) that also varies from 0 to 100 points. Methods for implementing cardiovascular health assessment and longitudinal monitoring are discussed, as are potential data sources and tools to promote widespread adoption in policy, public health, clinical, institutional, and community settings.

Status of Cardiovascular Health in US Adults and Children Using the American Heart Association's New "Life's Essential 8" Metrics: Prevalence Estimates From the National Health and Nutrition Examination Survey (NHANES), 2013 Through 2018.

BACKGROUND: The American Heart Association recently published an updated algorithm for quantifying cardiovascular health (CVH)-the Life's Essential 8 score. We quantified US levels of CVH using the new score. METHODS: We included individuals ages 2 through 79 years (not pregnant or institutionalized) who were free of cardiovascular disease from the National Health and Nutrition Examination Surveys in 2013 through 2018. For all participants, we calculated the overall CVH score (range, 0 [lowest] to 100 [highest]), as well as the score for each component of diet, physical activity, nicotine exposure, sleep duration, body mass index, blood lipids, blood glucose, and blood pressure, using published American Heart Association definitions. Sample weights and design were incorporated in calculating prevalence estimates and standard errors using standard survey procedures. CVH scores were assessed across strata of age, sex, race and ethnicity, family income, and depression. RESULTS: There were 23 409 participants, representing 201 728 000 adults and 74 435 000 children. The overall mean CVH score was 64.7 (95% CI, 63.9-65.6) among adults using all 8 metrics and 65.5 (95% CI, 64.4-66.6) for the 3 metrics available (diet, physical activity, and body mass index) among children and adolescents ages 2 through 19 years. For adults, there were significant differences in mean overall CVH scores by sex (women, 67.0; men, 62.5), age (range of mean values, 62.2-68.7), and racial and ethnic group (range, 59.7-68.5). Mean scores were lowest for diet, physical activity, and body mass index metrics. There were large differences in mean scores across demographic groups for diet (range, 23.8-47.7), nicotine exposure (range, 63.1-85.0), blood glucose (range, 65.7-88.1), and blood pressure (range, 49.5-84.0). In children, diet scores were low (mean 40.6) and were progressively lower in higher age groups (from 61.1 at ages 2 through 5 to 28.5 at ages 12 through 19); large differences were also noted in mean physical activity (range, 63.1-88.3) and body mass index (range, 74.4-89.4) scores by sociodemographic group. CONCLUSIONS: The new Life's Essential 8 score helps identify large group and individual differences in CVH. Overall CVH in the US population remains well below optimal levels and there are both broad and targeted opportunities to monitor, preserve, and improve CVH across the life course in individuals and the population.

Association of Cardiovascular Health Through Young Adulthood With Genome-Wide DNA Methylation Patterns in Midlife: The CARDIA Study.

BACKGROUND: Cardiovascular health (CVH) from young adulthood is strongly associated with an individual's future risk of cardiovascular disease (CVD) and total mortality. Defining epigenomic biomarkers of lifelong CVH exposure and understanding their roles in CVD development may help develop preventive and therapeutic strategies for CVD. METHODS: In 1085 CARDIA study (Coronary Artery Risk Development in Young Adults) participants, we defined a clinical cumulative CVH score that combines body mass index, blood pressure, total cholesterol, and fasting glucose measured longitudinally from young adulthood through middle age over 20 years (mean age, 25-45). Blood DNA methylation at >840 000 methylation markers was measured twice over 5 years (mean age, 40 and 45). Epigenome-wide association analyses on the cumulative CVH score were performed in CARDIA and compared in the FHS (Framingham Heart Study). We used penalized regression to build a methylation-based risk score to evaluate the risk of incident coronary artery calcification and clinical CVD events. RESULTS: We identified 45 methylation markers associated with cumulative CVH at false discovery rate <0.01 (P=4.7E-7-5.8E-17) in CARDIA and replicated in FHS. These associations were more pronounced with methylation measured at an older age. CPT1A, ABCG1, and SREBF1 appeared as the most prominent genes. The 45 methylation markers were mostly located in transcriptionally active chromatin and involved lipid metabolism, insulin secretion, and cytokine production pathways. Three methylation markers located in genes SARS1, SOCS3, and LINC-PINT statistically mediated 20.4% of the total effect between CVH and risk of incident coronary artery calcification. The methylation risk score added information and significantly (P=0.004) improved the discrimination capacity of coronary artery calcification status versus CVH score alone and showed association with risk of incident coronary artery calcification 5 to 10 years later independent of cumulative CVH score (odds ratio, 1.87; P=9.66E-09). The methylation risk score was also associated with incident clinical CVD in FHS (hazard ratio, 1.28; P=1.22E-05). CONCLUSIONS: Cumulative CVH from young adulthood contributes to midlife epigenetic programming over time. Our findings demonstrate the role of epigenetic markers in response to CVH changes and highlight the potential of epigenomic markers for precision CVD prevention, and earlier detection of subclinical CVD, as well.

Associations of Clinical and Social Risk Factors With Racial Differences in Premature Cardiovascular Disease.

BACKGROUND: Racial differences in cardiovascular disease (CVD) are likely related to differences in clinical and social factors. The relative contributions of these factors to Black-White differences in premature CVD have not been investigated. METHODS: In Black and White adults aged 18 to 30 years at baseline in the CARDIA study (Coronary Artery Risk Development in Young Adults), the associations of clinical, lifestyle, depression, socioeconomic, and neighborhood factors across young adulthood with racial differences in incident premature CVD were evaluated in sex-stratified, multivariable-adjusted Cox proportional hazards models using multiply imputed data assuming missing at random. Percent reduction in the ß estimate (log-hazard ratio [HR]) for race quantified the contribution of each factor group to racial differences in incident CVD. RESULTS: Among 2785 Black and 2327 White participants followed for a median 33.9 years (25th-75th percentile, 33.7-34.0), Black (versus White) adults had a higher risk of incident premature CVD (Black women: HR, 2.44 [95% CI, 1.71-3.49], Black men: HR, 1.59 [1.20-2.10] adjusted for age and center). Racial differences were not statistically significant after full adjustment (Black women: HR, 0.91 [0.55-1.52], Black men: HR 1.02 [0.70-1.49]). In women, the largest magnitude percent reduction in the ß estimate for race occurred with adjustment for clinical (87%), neighborhood (32%), and socioeconomic (23%) factors. In men, the largest magnitude percent reduction in the ß estimate for race occurred with an adjustment for clinical (64%), socioeconomic (50%), and lifestyle (34%) factors. CONCLUSIONS: In CARDIA, the significantly higher risk for premature CVD in Black versus White adults was statistically explained by adjustment for antecedent multilevel factors. The largest contributions to racial differences were from clinical and neighborhood factors in women, and clinical and socioeconomic factors in men.

Racial and Ethnic Differences in All-Cause and Cardiovascular Disease Mortality: The MESA Study.

BACKGROUND: Despite improvements in population health, marked racial and ethnic disparities in longevity and cardiovascular disease (CVD) mortality persist. This study aimed to describe risks for all-cause and CVD mortality by race and ethnicity, before and after accounting for socioeconomic status (SES) and other factors, in the MESA study (Multi-Ethnic Study of Atherosclerosis). METHODS: MESA recruited 6814 US adults, 45 to 84 years of age, free of clinical CVD at baseline, including Black, White, Hispanic, and Chinese individuals (2000-2002). Using Cox proportional hazards modeling with time-updated covariates, we evaluated the association of self-reported race and ethnicity with all-cause and adjudicated CVD mortality, with progressive adjustments for age and sex, SES (neighborhood SES, income, education, and health insurance), lifestyle and psychosocial risk factors, clinical risk factors, and immigration history. RESULTS: During a median of 15.8 years of follow-up, 22.8% of participants (n=1552) died, of which 5.3% (n=364) died of CVD. After adjusting for age and sex, Black participants had a 34% higher mortality hazard (hazard ratio [HR], 1.34 [95% CI, 1.19-1.51]), Chinese participants had a 21% lower mortality hazard (HR, 0.79 [95% CI, 0.66-0.95]), and there was no mortality difference in Hispanic participants (HR, 0.99 [95% CI, 0.86-1.14]) compared with White participants. After adjusting for SES, the mortality HR for Black participants compared with White participants was reduced (HR, 1.16 [95% CI, 1.01-1.34]) but still statistically significant. With adjustment for SES, the mortality hazards for Chinese and Hispanic participants also decreased in comparison with White participants. After further adjustment for additional risk factors and immigration history, Hispanic participants (HR, 0.77 [95% CI, 0.63-0.94]) had a lower mortality risk than White participants, and hazard ratios for Black participants (HR, 1.08 [95% CI, 0.92-1.26]) and Chinese participants (HR, 0.81 [95% CI, 0.60-1.08]) were not significantly different from those of White participants. Similar trends were seen for CVD mortality, although the age- and sex-adjusted HR for CVD mortality for Black participants compared with White participants was greater than all-cause mortality (HR, 1.72 [95% CI, 1.34-2.21] compared with HR, 1.34 [95% CI, 1.19-1.51]). CONCLUSIONS: These results highlight persistent racial and ethnic differences in overall and CVD mortality, largely attributable to social determinants of health, and support the need to identify and act on systemic factors that shape differences in health across racial and ethnic groups.

Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans.

BACKGROUND: Treatment and preventative advances for chronic obstructive pulmonary disease (COPD) have been slow due, in part, to limited subphenotypes. We tested if unsupervised machine learning on CT images would discover CT emphysema subtypes with distinct characteristics, prognoses and genetic associations. METHODS: New CT emphysema subtypes were identified by unsupervised machine learning on only the texture and location of emphysematous regions on CT scans from 2853 participants in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS), a COPD case-control study, followed by data reduction. Subtypes were compared with symptoms and physiology among 2949 participants in the population-based Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study and with prognosis among 6658 MESA participants. Associations with genome-wide single-nucleotide-polymorphisms were examined. RESULTS: The algorithm discovered six reproducible (interlearner intraclass correlation coefficient, 0.91-1.00) CT emphysema subtypes. The most common subtype in SPIROMICS, the combined bronchitis-apical subtype, was associated with chronic bronchitis, accelerated lung function decline, hospitalisations, deaths, incident airflow limitation and a gene variant near DRD1, which is implicated in mucin hypersecretion (p=1.1 ×10-8). The second, the diffuse subtype was associated with lower weight, respiratory hospitalisations and deaths, and incident airflow limitation. The third was associated with age only. The fourth and fifth visually resembled combined pulmonary fibrosis emphysema and had distinct symptoms, physiology, prognosis and genetic associations. The sixth visually resembled vanishing lung syndrome. CONCLUSION: Large-scale unsupervised machine learning on CT scans defined six reproducible, familiar CT emphysema subtypes that suggest paths to specific diagnosis and personalised therapies in COPD and pre-COPD.

Atherosclerotic Cardiovascular Disease or Heart Failure: First Cardiovascular Event in Adults With Prediabetes and Diabetes.

BACKGROUND: Individuals with prediabetes and diabetes are at increased risk of atherosclerotic cardiovascular disease (ASCVD) and heart failure (HF). Whether ASCVD or HF is more likely to occur first in these populations within different race-sex groups is unknown. OBJECTIVE: To determine the competing risk for the first cardiovascular event by subtype in Black and white men and women with prediabetes and diabetes. METHODS: Individual-level data from adults without ASCVD or HF were pooled from 6 population-based cohorts. We estimated the competing cumulative incidences of ASCVD, HF and noncardiovascular death as the first event in middle-aged (40-59 years) and older (60-79 years) adults, stratified by race and sex, with normal fasting plasma glucose (FPG < 100 mg/dL), prediabetes (FPG 100-125 mg/dL) and diabetes (FPG ≥ 126 mg/dL or on antihyperglycemic agents) at baseline. Within each race-sex group, we estimated risk the adjusted hazard ratio of ASCVD, HF and noncardiovascular death in adults with prediabetes and diabetes relative to adults with normoglycemia after adjusting for cardiovascular risk factors. RESULTS: In 40,117 participants with 638,910 person-years of follow-up, 5781 cases of incident ASCVD and 3179 cases of incident HF occurred. In middle-aged adults with diabetes, competing cumulative incidence of ASCVD as a first event was higher than HF in white men (35.4% vs 11.6%), Black men (31.6% vs 15.1%) and white women (24.3% vs 17.2%) but not in Black women (26.4% vs 28.4%). Within each group, the adjusted hazard ratio of ASCVD and HF was significantly higher in adults with diabetes than in adults with normal FPG levels. Findings were largely similar in middle-aged adults with prediabetes and older adults with prediabetes or diabetes. CONCLUSIONS: Black women with diabetes are more likely to develop HF as their first CVD event, whereas individuals with diabetes from other race-sex groups are more likely to present first with ASCVD. These results can inform the tailoring of primary prevention therapies for either HF- or ASCVD-specific pathways based on individual-level risk.

Multiple Prior Live Births Are Associated With Cardiac Remodeling and Heart Failure Risk in Women.

OBJECTIVE: Greater parity has been associated with cardiovascular disease risk. We sought to find whether the effects on cardiac remodeling and heart failure risk are clear. METHODS: We examined the association of number of live births with echocardiographic measures of cardiac structure and function in participants of the Framingham Heart Study (FHS) using multivariable linear regression. We next examined the association of parity with incident heart failure with preserved (HFpEF) or reduced (HFrEF) ejection fraction using a Fine-Gray subdistribution hazards model in a pooled analysis of n = 12,635 participants in the FHS, the Cardiovascular Health Study, the Multi-Ethnic Study of Atherosclerosis, and Prevention of Renal and Vascular Endstage Disease. Secondary analyses included major cardiovascular disease, myocardia infarction and stroke. RESULTS: Among n = 3931 FHS participants (mean age 48 ± 13 years), higher numbers of live births were associated with worse left ventricular fractional shortening (multivariable ß -1.11 (0.31); P = 0.0005 in ≥ 5 live births vs nulliparous women) and worse cardiac mechanics, including global circumferential strain and longitudinal and radial dyssynchrony (P < 0.01 for all comparing ≥ 5 live births vs nulliparity). When examining HF subtypes, women with ≥ 5 live births were at higher risk of developing future HFrEF compared with nulliparous women (HR 1.93, 95% CI 1.19-3.12; P = 0.008); by contrast, a lower risk of HFpEF was observed (HR 0.58, 95% CI 0.37-0.91; P = 0.02). CONCLUSIONS: Greater numbers of live births are associated with worse cardiac structure and function. There was no association with overall HF, but a higher number of live births was associated with greater risk for incident HFrEF.

Neurodevelopmental vulnerability to psychosis: developmentally-based methods enable detection of early life inhibitory control deficits that predict psychotic-like experiences at the transition to adolescence.

BACKGROUND: Inhibitory control develops in early childhood, and atypical development may be a measurable marker of risk for the later development of psychosis. Additionally, inhibitory control may be a target for intervention. METHODS: Behavioral performance on a developmentally appropriate Go/No-Go task including a frustration manipulation completed by children ages 3-5 years (early childhood; n = 107) was examined in relation to psychotic-like experiences (PLEs; 'tween'; ages 9-12), internalizing symptoms, and externalizing symptoms self-reported at long-term follow-up (pre-adolescence; ages 8-11). ERP N200 amplitude for a subset of these children (n = 34) with electrophysiological data during the task was examined as an index of inhibitory control. RESULTS: Children with lower accuracy on No-Go trials compared to Go trials in early childhood (F(1,101) = 3.976, p = 0.049), evidenced higher PLEs at the transition to adolescence 4-9 years later, reflecting a specific deficit in inhibitory control. No association was observed with internalizing or externalizing symptoms. Decreased accuracy during the frustration manipulation predicted higher internalizing, F(2,202) = 5.618, p = 0.004, and externalizing symptoms, F(2,202) = 4.663, p = 0.010. Smaller N200 amplitudes were observed on No-Go trials for those with higher PLEs, F(1,101) = 6.075, p = 0.020; no relationship was observed for internalizing or externalizing symptoms. CONCLUSIONS: Long-term follow-up demonstrates for the first time a specific deficit in inhibitory control behaviorally and electrophysiology, for individuals who later report more PLEs. Decreases in task performance under frustration induction indicated risk for internalizing and externalizing symptoms. These findings suggest that pathophysiological mechanisms for psychosis are relevant and discriminable in early childhood, and further, suggest an identifiable and potentially modifiable target for early intervention.

Epigenetic Age Acceleration Reflects Long-Term Cardiovascular Health.

[Figure: see text].

Comparison of State-of-the-Art Neural Network Survival Models with the Pooled Cohort Equations for Cardiovascular Disease Risk Prediction.

BACKGROUND: The Pooled Cohort Equations (PCEs) are race- and sex-specific Cox proportional hazards (PH)-based models used for 10-year atherosclerotic cardiovascular disease (ASCVD) risk prediction with acceptable discrimination. In recent years, neural network models have gained increasing popularity with their success in image recognition and text classification. Various survival neural network models have been proposed by combining survival analysis and neural network architecture to take advantage of the strengths from both. However, the performance of these survival neural network models compared to each other and to PCEs in ASCVD prediction is unknown. METHODS: In this study, we used 6 cohorts from the Lifetime Risk Pooling Project (with 5 cohorts as training/internal validation and one cohort as external validation) and compared the performance of the PCEs in 10-year ASCVD risk prediction with an all two-way interactions Cox PH model (Cox PH-TWI) and three state-of-the-art neural network survival models including Nnet-survival, Deepsurv, and Cox-nnet. For all the models, we used the same 7 covariates as used in the PCEs. We fitted each of the aforementioned models in white females, white males, black females, and black males, respectively. We evaluated models' internal and external discrimination power and calibration. RESULTS: The training/internal validation sample comprised 23216 individuals. The average age at baseline was 57.8 years old (SD = 9.6); 16% developed ASCVD during average follow-up of 10.50 (SD = 3.02) years. Based on 10 × 10 cross-validation, the method that had the highest C-statistics was Deepsurv (0.7371) for white males, Deepsurv and Cox PH-TWI (0.7972) for white females, PCE (0.6981) for black males, and Deepsurv (0.7886) for black females. In the external validation dataset, Deepsurv (0.7032), Cox-nnet (0.7282), PCE (0.6811), and Deepsurv (0.7316) had the highest C-statistics for white male, white female, black male, and black female population, respectively. Calibration plots showed that in 10 × 10 validation, all models had good calibration in all race and sex groups. In external validation, all models overestimated the risk for 10-year ASCVD. CONCLUSIONS: We demonstrated the use of the state-of-the-art neural network survival models in ASCVD risk prediction. Neural network survival models had similar if not superior discrimination and calibration compared to PCEs.

Race/Ethnicity, Spirometry Reference Equations, and Prediction of Incident Clinical Events: The Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study.

Rationale: Normal values for FEV1 and FVC are currently calculated using cross-sectional reference equations that include terms for race/ethnicity, an approach that may reinforce disparities and is of unclear clinical benefit. Objectives: To determine whether race/ethnicity-based spirometry reference equations improve the prediction of incident chronic lower respiratory disease (CLRD) events and mortality compared with race/ethnicity-neutral equations. Methods: The MESA Lung Study, a population-based, prospective cohort study of White, Black, Hispanic, and Asian adults, performed standardized spirometry from 2004 to 2006. Predicted values for spirometry were calculated using race/ethnicity-based equations following guidelines and, alternatively, race/ethnicity-neutral equations without terms for race/ethnicity. Participants were followed for events through 2019. Measurements and Main Results: The mean age of 3,344 participants was 65 years, and self-reported race/ethnicity was 36% White, 25% Black, 23% Hispanic, and 17% Asian. There were 181 incident CLRD-related events and 547 deaths over a median of 11.6 years. There was no evidence that percentage predicted FEV1 or FVC calculated using race/ethnicity-based equations improved the prediction of CLRD-related events compared with those calculated using race/ethnicity-neutral equations (difference in C statistics for FEV1, -0.005; 95% confidence interval [CI], -0.013 to 0.003; difference in C statistic for FVC, -0.008; 95% CI, -0.016 to -0.0006). Findings were similar for mortality (difference in C statistics for FEV1, -0.002; 95% CI, -0.008 to 0.003; difference in C statistics for FVC, -0.004; 95% CI, -0.009 to 0.001). Conclusions: There was no evidence that race/ethnicity-based spirometry reference equations improved the prediction of clinical events compared with race/ethnicity-neutral equations. The inclusion of race/ethnicity in spirometry reference equations should be reconsidered.

Depressive Symptom Subgroups and Their Association with Prevalent and Incident Cardiovascular Risk Factors in the Coronary Artery Risk Development in Young Adults (CARDIA) Study.

BACKGROUND: We sought to identify depressive symptom subgroups in a community sample of young adults, investigate their stability over time, and determine their association with prevalent and incident cardiovascular disease (CVD) risk factors. METHOD: Participants were 3377 adults from the Coronary Artery Risk Development in Young Adults study. Using latent class and latent transition analysis, we derived subgroups based on items of the 20-item version of the Center for Epidemiologic Studies Depression Scale in 1990, and examined patterns of change over a 10-year period (1990-2000). Cox regression models were used to examine associations between subgroup membership and prevalent (2000) and incident (2000 to 2016) obesity, hypertension, and diabetes. RESULTS: Three baseline subgroups were identified and labeled: "No Symptoms" (63.5%), "Lack of Positive Affect" (PA, 25.6%), and "Depressed Mood" (10.9%). At 10-year follow-up, individuals in "No Symptoms" subgroup had the highest probability (0.84) of being classified within the same subgroup. Participants classified as "Lack of PA" were likely (0.46) to remain in the same subgroup or be classified as "No Symptoms." Participants in the "Depressed Mood" were most likely to transition to the "Lack of PA" subgroup (0.38). Overall, 30.5% of participants transitioned between subgroups, with 11.4% classified as "Worsening" and 19.1% as "Improving." Relative to the "No Symptoms Stable," other subgroups ("Depressed Stable," "Worsening," and "Improving") were associated with prevalent obesity and hypertension. CONCLUSION: We identified distinct depressive symptom subgroups that are variably stable over time, and their change patterns were differentially associated with CVD risk factor prevalence.

Predictive Utility of Irritability "In Context": Proof-of-Principle for an Early Childhood Mental Health Risk Calculator.

OBJECTIVE: We provide proof-of-principle for a mental health risk calculator advancing clinical utility of the irritability construct for identification of young children at high risk for common, early onsetting syndromes. METHOD: Data were harmonized from two longitudinal early childhood subsamples (total N = 403; 50.1% Male; 66.7% Nonwhite; Mage = 4.3 years). The independent subsamples were clinically enriched via disruptive behavior and violence (Subsample 1) and depression (Subsample 2). In longitudinal models, epidemiologic risk prediction methods for risk calculators were applied to test the utility of the transdiagnostic indicator, early childhood irritability, in the context of other developmental and social-ecological indicators to predict risk of internalizing/externalizing disorders at preadolescence (Mage = 9.9 years). Predictors were retained when they improved model discrimination (area under the receiver operating characteristic curve [AUC] and integrated discrimination index [IDI]) beyond the base demographic model. RESULTS: Compared to the base model, the addition of early childhood irritability and adverse childhood experiences significantly improved the AUC (0.765) and IDI slope (0.192). Overall, 23% of preschoolers went on to develop a preadolescent internalizing/externalizing disorder. For preschoolers with both elevated irritability and adverse childhood experiences, the likelihood of an internalizing/externalizing disorder was 39-66%. CONCLUSIONS: Predictive analytic tools enable personalized prediction of psychopathological risk for irritable young children, holding transformative potential for clinical translation.

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.

PDAY risk score predicts cardiovascular events in young adults: the CARDIA study.

AIMS: Atherosclerotic cardiovascular disease (ASCVD) risk prediction equations apply to older adults. For this study, the Pathobiologic Determinants of Atherosclerosis in Youth (PDAY) risk score, based on post-mortem measurements of atherosclerosis in 15-34-year olds dying accidentally, was used to predict ASCVD events, specifically myocardial infarction and revascularization, in middle age, from risk measured at ≤40 years of age. METHODS AND RESULTS: The Coronary Artery Risk Development in Young Adults Study (CARDIA) collected longitudinal cardiovascular risk data, coronary artery calcium (CAC) scores, and ASCVD data beginning at age 18 and 30 years with 30-year follow-up. Predictive accuracy for ASCVD of the PDAY risk score, calculated at baseline (mean age 24) and at all six CARDIA examinations up until year 15, was examined. We also examined whether the presence of CAC improved model discrimination. The cohort for this study comprised 5004 Black and White men and women, at baseline and 3558 with data at year 15. Each standard deviation increase in PDAY score, at each examination, was significantly associated with future ASCVD. Hazard ratios (per standard deviation) increased from 1.74 to 2.04 from year 0 to year 15. C-statistics ranged from 0.771 to 0.794. Coronary artery calcium measurement at age 33-45 years improved risk prediction only if the score was 0. Cumulative risk exposure over the first 15 years of the CARDIA study also had high-predictive value (c-statistic 0.798, 95% confidence interval 0.762-0.835). CONCLUSION: The PDAY risk score may be used in young adults, prior to age 40 years to predict ASCVD events.