ABSTRACT
OBJECTIVE: Changes in cardiovascular health (CVH) during the life course are associated with future cardiovascular disease (CVD). Longitudinal clustering analysis using subgraph augmented non-negative matrix factorization (SANMF) could create phenotypic risk profiles of clustered CVH metrics. MATERIALS AND METHODS: Life's Essential 8 (LE8) variables, demographics, and CVD events were queried over 15 ears in 5060 CARDIA participants with 18 years of subsequent follow-up. LE8 subgraphs were mined and a SANMF algorithm was applied to cluster frequently occurring subgraphs. K-fold cross-validation and diagnostics were performed to determine cluster assignment. Cox proportional hazard models were fit for future CV event risk and logistic regression was performed for cluster phenotyping. RESULTS: The cohort (54.6% female, 48.7% White) produced 3 clusters of CVH metrics: Healthy & Late Obesity (HLO) (29.0%), Healthy & Intermediate Sleep (HIS) (43.2%), and Unhealthy (27.8%). HLO had 5 ideal LE8 metrics between ages 18 and 39 years, until BMI increased at 40. HIS had 7 ideal LE8 metrics, except sleep. Unhealthy had poor levels of sleep, smoking, and diet but ideal glucose. Race and employment were significantly different by cluster (P < .001) but not sex (P = .734). For 301 incident CV events, multivariable hazard ratios (HRs) for HIS and Unhealthy were 0.73 (0.53-1.00, P = .052) and 2.00 (1.50-2.68, P < .001), respectively versus HLO. A 15-year event survival was 97.0% (HIS), 96.3% (HLO), and 90.4% (Unhealthy, P < .001). DISCUSSION AND CONCLUSION: SANMF of LE8 metrics identified 3 unique clusters of CVH behavior patterns. Clustering of longitudinal LE8 variables via SANMF is a robust tool for phenotypic risk assessment for future adverse cardiovascular events.
Subject(s)
Cardiovascular Diseases , Quality Indicators, Health Care , Humans , Female , United States , Male , Unsupervised Machine Learning , Cardiovascular Diseases/epidemiology , Diet , Cluster Analysis , Risk FactorsABSTRACT
Introduction: Most current clinical risk prediction scores for cardiovascular disease prevention use a composite outcome. Risk prediction scores for specific cardiovascular events could identify people who are at higher risk for some events than others informing personalized care and trial recruitment. We sought to predict risk for multiple different events, describe how those risks differ, and examine if these differences could improve treatment priorities. Methods: We used participant-level data from five cohort studies. We included participants between 40 and 79 years old who had no history of myocardial infarction (MI), stroke, or heart failure (HF). We made separate models to predict 10-year rates of first atherosclerotic cardiovascular disease (ASCVD), first fatal or nonfatal MI, first fatal or nonfatal stroke, new-onset HF, fatal ASCVD, fatal MI, fatal stroke, and all-cause mortality using established ASCVD risk factors. To limit overfitting, we used elastic net regularization with alpha = 0.75. We assessed the models for calibration, discrimination, and for correlations between predicted risks for different events. We also estimated the potential impact of varying treatment based on patients who are high risk for some ASCVD events, but not others. Results: Our study included 24,505 people; 55.6% were women, and 20.7% were non-Hispanic Black. Our models had C-statistics between 0.75 for MI and 0.85 for HF, good calibration, and minimal overfitting. The models were least similar for fatal stroke and all MI (0.58). In 1,840 participants whose risk of MI but not stroke or all-cause mortality was in the top quartile, we estimate one blood pressure-lowering medication would have a 2.4% chance of preventing any ASCVD event per 10 years. A moderate-strength statin would have a 2.1% chance. In 1,039 participants who had top quartile risk of stroke but not MI or mortality, a blood pressure-lowering medication would have a 2.5% chance of preventing an event, but a moderate-strength statin, 1.6%. Conclusion: We developed risk scores for eight key clinical events and found that cardiovascular risk varies somewhat for different clinical events. Future work could determine if tailoring decisions by risk of separate events can improve care.
ABSTRACT
BACKGROUND: Ethnic differences in cognitive decline have been reported. Whether they can be explained by differences in systolic blood pressure (SBP) is uncertain. OBJECTIVE: Determine whether cumulative mean SBP levels explain differences in cognitive decline between Hispanic and White individuals. METHODS: Pooled cohort study of individual participant data from six cohorts (1971-2017). The present study reports results on SBP and cognition among Hispanic and White individuals. Outcomes were changes in global cognition (GC) (primary), executive function (EF) (secondary), and memory standardized as t-scores (mean [SD], 50 [10]); a 1-point difference represents a 0.1 SD difference in cognition. Median follow-up was 7.7 (Q1-Q3, 5.2-20.1) years. RESULTS: We included 24,570 participants free of stroke and dementia: 2,475 Hispanic individuals (median age, cumulative mean SBP at first cognitive assessment, 67 years, 132.5 mmHg; 40.8% men) and 22,095 White individuals (60 years,134 mmHg; 47.3% men). Hispanic individuals had slower declines in GC, EF, and memory than White individuals when all six cohorts were examined. Two cohorts recruited Hispanic individuals by design. In a sensitivity analysis, Hispanic individuals in these cohorts had faster decline in GC, similar decline in EF, and slower decline in memory than White individuals. Higher time-varying cumulative mean SBP was associated with faster declines in GC, EF, and memory in all analyses. After adjusting for time-varying cumulative mean SBP, differences in cognitive slopes between Hispanic and White individuals did not change. CONCLUSION: We found no evidence that cumulative mean SBP differences explained differences in cognitive decline between Hispanic and White individuals.
Subject(s)
Blood Pressure , Cognition , Aged , Blood Pressure/physiology , Cognition/physiology , Cohort Studies , Female , Hispanic or Latino , Humans , Male , Risk Factors , White PeopleABSTRACT
The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) is a national prospective study of adults comprising 14 established US prospective cohort studies. Starting as early as 1971, investigators in the C4R cohort studies have collected data on clinical and subclinical diseases and their risk factors, including behavior, cognition, biomarkers, and social determinants of health. C4R links this pre-coronavirus disease 2019 (COVID-19) phenotyping to information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and acute and postacute COVID-related illness. C4R is largely population-based, has an age range of 18-108 years, and reflects the racial, ethnic, socioeconomic, and geographic diversity of the United States. C4R ascertains SARS-CoV-2 infection and COVID-19 illness using standardized questionnaires, ascertainment of COVID-related hospitalizations and deaths, and a SARS-CoV-2 serosurvey conducted via dried blood spots. Master protocols leverage existing robust retention rates for telephone and in-person examinations and high-quality event surveillance. Extensive prepandemic data minimize referral, survival, and recall bias. Data are harmonized with research-quality phenotyping unmatched by clinical and survey-based studies; these data will be pooled and shared widely to expedite collaboration and scientific findings. This resource will allow evaluation of risk and resilience factors for COVID-19 severity and outcomes, including postacute sequelae, and assessment of the social and behavioral impact of the pandemic on long-term health trajectories.
Subject(s)
COVID-19 , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , Cohort Studies , Humans , Middle Aged , Pandemics , Prospective Studies , SARS-CoV-2 , United States/epidemiology , Young AdultABSTRACT
BACKGROUND: Both parental and neighbourhood socio-economic status (SES) are linked to poorer health independently of personal SES measures, but the biological mechanisms are unclear. Our objective was to examine these influences via epigenetic age acceleration (EAA)-the discrepancy between chronological and epigenetic ages. METHODS: We examined three USA-based [Coronary Artery Risk Disease in Adults (CARDIA) study, Fragile Families and Child Wellbeing Study (FFCWS) and Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS)] and one Mexico-based (Project Viva) cohort. DNA methylation was measured using Illumina arrays, personal/parental SES by questionnaire and neighbourhood disadvantage from geocoded address. In CARDIA, we examined the most strongly associated personal, parental and neighbourhood SES measures with EAA (Hannum's method) at study years 15 and 20 separately and combined using a generalized estimating equation (GEE) and compared with other EAA measures (Horvath's EAA, PhenoAge and GrimAge calculators, and DunedinPoAm). RESULTS: EAA was associated with paternal education in CARDIA [GEEs: ßsome college = -1.01 years (-1.91, -0.11) and ßSubject(s)
Aging
, DNA Methylation
, Adolescent
, Adult
, Aging/genetics
, Child
, Cohort Studies
, Educational Status
, Epigenesis, Genetic
, Female
, Humans
, Male
, Mexico/epidemiology
ABSTRACT
OBJECTIVE: The Pooled Cohort Equations (PCE) serve as the platform for quantitative risk assessment for atherosclerotic cardiovascular disease (ASCVD). Data are sparse regarding the benefit of adding health-related quality of life (HRQoL) measures to the PCE. We sought to estimate the association of HRQoL with ASCVD events and evaluate the potential utility of adding HRQoL to the PCE in refining quantitative risk assessment for primary prevention decisions. METHODS: Three multi-ethnic longitudinal cohorts were included in the study. HRQoL was measured using the SF-12 physical component summary (PCS) and mental component summary (MCS); higher PCS or MCS scores indicate better HRQoL. We constructed a four-level HRQoL status variable: MCS <50 and PCS <50; MCS <50 and PCS ≥50; MCS ≥50 and PCS <50; MCS ≥50 and PCS ≥50. Harrell's C statistics and net reclassification improvement (NRI) analyses were used to assess the added predictive ability of HRQoL for incident ASCVD. RESULTS: A total of 9,904 individuals were included in the analysis, of whom 4,743 were in the low risk subgroup (<5% predicted 10-year risk). HRQoL status, PCS and its subscale scores were independent predictors of ASCVD events. HRQoL improved both discrimination (delta C: 0.004, p = 0.05) and reclassification (cNRI: 0.15, p<0.01) modestly when added to PCE; 3% and 6% of individuals with events were correctly reclassified to higher risk in the overall sample and low risk subgroup, respectively. CONCLUSION: HRQoL is an independent predictor of ASCVD events, and improves ASCVD risk prediction significantly, though modestly, overall and in low-risk individuals. HRQoL may be a cost-effective risk-enhancing factor for refining quantitative risk assessment for primary prevention decisions.
ABSTRACT
The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) is a national prospective study of adults at risk for coronavirus disease 2019 (COVID-19) comprising 14 established United States (US) prospective cohort studies. For decades, C4R cohorts have collected extensive data on clinical and subclinical diseases and their risk factors, including behavior, cognition, biomarkers, and social determinants of health. C4R will link this pre-COVID phenotyping to information on SARS-CoV-2 infection and acute and post-acute COVID-related illness. C4R is largely population-based, has an age range of 18-108 years, and broadly reflects the racial, ethnic, socioeconomic, and geographic diversity of the US. C4R is ascertaining severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and COVID-19 illness using standardized questionnaires, ascertainment of COVID-related hospitalizations and deaths, and a SARS-CoV-2 serosurvey via dried blood spots. Master protocols leverage existing robust retention rates for telephone and in-person examinations, and high-quality events surveillance. Extensive pre-pandemic data minimize referral, survival, and recall bias. Data are being harmonized with research-quality phenotyping unmatched by clinical and survey-based studies; these will be pooled and shared widely to expedite collaboration and scientific findings. This unique resource will allow evaluation of risk and resilience factors for COVID-19 severity and outcomes, including post-acute sequelae, and assessment of the social and behavioral impact of the pandemic on long-term trajectories of health and aging.
ABSTRACT
The association between dietary sodium and potassium intake with the development of kidney disease remains unclear, particularly among younger individuals. Here, we determined whether dietary sodium and potassium intake are associated with incident chronic kidney disease (CKD) using data from 1,030 adults (age 23-35 in 1990-1991) from the Coronary Artery Risk Development In Young Adults study, based on repeated measurements of estimated glomerular filtration rate (eGFR) and urinary albumin to creatinine ratio (ACR) from 1995 through 2015. Urinary sodium and potassium excretion (mg/day), calculated from three 24-hour urine collections in 1990-1991, were averaged to measure sodium and potassium intake. Serum creatinine was used to calculate eGFR using the CKD EPI equation; spot urine albumin and creatinine were used to calculate ACR, each at five visits from 1995-1996 through 2015-2016. CKD was defined as decreased eGFR (under 60 ml/min/1.73m2) or the development of albuminuria (ACR over 30 mg/g). We used log binomial regression models adjusted for socio-demographic, behavioral, and clinical factors to determine whether sodium and potassium intake were associated with incident CKD (decreased eGFR or developed albuminuria) among those free of CKD in 1995. Dietary sodium intake was not significantly associated with incident CKD. However, every 1,000 mg/day increment of potassium intake in 1990 was significantly associated with a 29% lower risk of incident albuminuria (relative risk 0.71, 95% confidence interval 0.53, 0.95), but not eGFR. Thus, higher dietary potassium intake may protect against the development of kidney damage, particularly albuminuria.
Subject(s)
Potassium, Dietary , Renal Insufficiency, Chronic , Adult , Albuminuria/epidemiology , Creatinine , Glomerular Filtration Rate , Humans , Kidney , Renal Insufficiency, Chronic/diagnosis , Renal Insufficiency, Chronic/epidemiology , Renal Insufficiency, Chronic/prevention & control , Young AdultABSTRACT
Importance: Black individuals are more likely than white individuals to develop dementia. Whether higher blood pressure (BP) levels in black individuals explain differences between black and white individuals in dementia risk is uncertain. Objective: To determine whether cumulative BP levels explain racial differences in cognitive decline. Design, Setting, and Participants: Individual participant data from 5 cohorts (January 1971 to December 2017) were pooled from the Atherosclerosis Risk in Communities Study, Coronary Artery Risk Development in Young Adults Study, Cardiovascular Health Study, Framingham Offspring Study, and Northern Manhattan Study. Outcomes were standardized as t scores (mean [SD], 50 [10]); a 1-point difference represented a 0.1-SD difference in cognition. The median (interquartile range) follow-up was 12.4 (5.9-21.0) years. Analysis began September 2018. Main Outcomes and Measures: The primary outcome was change in global cognition, and secondary outcomes were change in memory and executive function. Exposures: Race (black vs white). Results: Among 34â¯349 participants, 19â¯378 individuals who were free of stroke and dementia and had longitudinal BP, cognitive, and covariate data were included in the analysis. The mean (SD) age at first cognitive assessment was 59.8 (10.4) years and ranged from 5 to 95 years. Of 19â¯378 individuals, 10â¯724 (55.3%) were female and 15â¯526 (80.1%) were white. Compared with white individuals, black individuals had significantly faster declines in global cognition (-0.03 points per year faster [95% CI, -0.05 to -0.01]; P = .004) and memory (-0.08 points per year faster [95% CI, -0.11 to -0.06]; P < .001) but significantly slower declines in executive function (0.09 points per year slower [95% CI, 0.08-0.10]; P < .001). Time-dependent cumulative mean systolic BP level was associated with significantly faster declines in global cognition (-0.018 points per year faster per each 10-mm Hg increase [95% CI, -0.023 to -0.014]; P < .001), memory (-0.028 points per year faster per each 10-mm Hg increase [95% CI, -0.035 to -0.021]; P < .001), and executive function (-0.01 points per year faster per each 10-mm Hg increase [95% CI, -0.014 to -0.007]; P < .001). After adjusting for cumulative mean systolic BP, differences between black and white individuals in cognitive slopes were attenuated for global cognition (-0.01 points per year [95% CI, -0.03 to 0.01]; P = .56) and memory (-0.06 points per year [95% CI, -0.08 to -0.03]; P < .001) but not executive function (0.10 points per year [95% CI, 0.09-0.11]; P < .001). Conclusions and Relevance: These results suggest that black individuals' higher cumulative BP levels may contribute to racial differences in later-life cognitive decline.
Subject(s)
Blood Pressure/physiology , Cognitive Dysfunction/ethnology , Adolescent , Adult , Black or African American , Aged , Aged, 80 and over , Child , Child, Preschool , Cognitive Dysfunction/physiopathology , Cohort Studies , Female , Humans , Longitudinal Studies , Male , Middle Aged , Risk Factors , White People , Young AdultABSTRACT
BACKGROUND: The metabolic syndrome (MetS) is a collection of metabolic disturbances that can lead to various cardiovascular diseases. Previous studies have shown a more adverse metabolic risk profile is associated with more advanced biological aging. The associations between epigenetic biomarkers of age with MetS, however, are not well understood. We therefore investigated the associations between epigenetic age acceleration and MetS severity score and incident MetS. RESULTS: A subset of study participants with available whole blood at examination years 15 and 20 from the Coronary Artery Risk Development in Young Adults Study underwent epigenomic profiling using the Illumina MethylationEPIC Beadchip (~ 850,000 sites). Intrinsic and extrinsic epigenetic age acceleration (IEAA and EEAA) were calculated from DNA methylation levels. The MetS severity score was positively associated with IEAA at years 15 (P = 0.016) and 20 (P = 0.016) and EEAA at year 20 (P = 0.040) in cross-sectional analysis. IEAA at year 20 was significantly associated with incident MetS at year 30 (OR = 1.05 [95% CI 1.01, 1.10], P = 0.028). CONCLUSIONS: To our knowledge, this is the first report of the longitudinal association between epigenetic age acceleration and MetS. These findings suggest that a higher MetS severity score is associated with accelerated epigenetic aging and such aging may play a role in the development of metabolic disorders, potentially serving as a useful biomarker of and early detection tool for future MetS.
Subject(s)
Aging/genetics , Cardiovascular Diseases/etiology , DNA Methylation , Metabolic Syndrome/genetics , Adolescent , Adult , Cardiovascular Diseases/genetics , Cross-Sectional Studies , Epigenesis, Genetic , Epigenomics/methods , Female , Humans , Longitudinal Studies , Male , Metabolic Syndrome/complications , Middle Aged , Severity of Illness Index , Young AdultABSTRACT
Evidence suggests an association between exposure to cadmium and dysglycemia. To investigate this matter, we examined the relationship between urinary cadmium and prediabetes in the cross sectional National Health and Nutrition Examination Survey (NHANES). NHANES participants for the years 2005 through 2010 aged ≥ 40 years were included in the analysis. Participants with nephropathy, overt diabetes, or missing required data were excluded. To assess the non-linear relationship between cadmium and Prediabetes, non-parametric logistic regression with B spline expansion of urinary cadmium/creatinine ratio was performed. This analysis revealed a complex non-linear association between higher cadmium levels and prediabetes. This relationship persisted, though with varying magnitudes across smoking groups (never smokers, moderate smokers, heavy smokers). In a conventional logistic regression analysis, this relationship was less evident with significantly increased OR for prediabetes was found in the highest quintile of urine cadmium compared to the lowest quintile in the overall population and in moderate smokers. In an age stratified analysis, a significant linear association was found only in the age groups 60-69 and ≥ 70. We conclude that there is a significant non-linear, complex relationship between urinary Cd levels, age, smoking habits and odds of prediabetes.
Subject(s)
Cadmium/adverse effects , Environmental Exposure/adverse effects , Prediabetic State/etiology , Age Factors , Aged , Blood Glucose/metabolism , Cadmium/urine , Cross-Sectional Studies , Environmental Exposure/analysis , Female , Humans , Insulin Resistance , Logistic Models , Male , Middle Aged , Nutrition Surveys , Odds Ratio , Prediabetic State/urine , Smoking/adverse effectsABSTRACT
BACKGROUND AND PURPOSE: Many studies use medical record review for ascertaining outcomes. One large, longitudinal study, the Women's Health Initiative (WHI), ascertains strokes using participant self-report and subsequent physician review of medical records. This is resource-intensive. Herein, we assess whether Medicare data can reliably assess stroke events in the WHI. METHODS: Subjects were WHI participants with fee-for-service Medicare. Four stroke definitions were created for Medicare data using discharge diagnoses in hospitalization claims: definition 1, stroke codes in any position; definition 2, primary position stroke codes; and definitions 3 and 4, hemorrhagic and ischemic stroke codes, respectively. WHI data were randomly split into training (50%) and test sets. A concordance matrix was used to examine the agreement between WHI and Medicare stroke diagnosis. A WHI stroke and a Medicare stroke were considered a match if they occurred within ±7 days of each other. Refined analyses excluded Medicare events when medical records were unavailable for comparison. RESULTS: Training data consisted of 24 428 randomly selected participants. There were 577 WHI strokes and 557 Medicare strokes using definition 1. Of these, 478 were a match. With regard to algorithm performance, specificity was 99.7%, negative predictive value was 99.7%, sensitivity was 82.8%, positive predictive value was 85.8%, and κ=0.84. Performance was similar for test data. Whereas specificity and negative predictive value exceeded 99%, sensitivity ranged from 75% to 88% and positive predictive value ranged from 80% to 90% across stroke definitions. CONCLUSIONS: Medicare data seem useful for population-based stroke research; however, performance characteristics depend on the definition selected.
Subject(s)
Medicare/statistics & numerical data , Physicians , Stroke/therapy , Women's Health , Aged , Algorithms , Brain Ischemia/therapy , Data Interpretation, Statistical , Female , Follow-Up Studies , Hospitalization , Humans , Intracranial Hemorrhages/therapy , Longitudinal Studies , Middle Aged , Reproducibility of Results , Surveys and Questionnaires , Treatment Outcome , United StatesABSTRACT
OBJECTIVE: Examine the association between multiple psychological factors (depressive symptoms, trait anxiety, perceived stress) and subclinical atherosclerosis in older age. METHOD: This cross-sectional study included 1101 adults ages 65-84 from the Chicago Healthy Aging Study (CHAS - 2007-2010). Previously validated self-report instruments were used to assess psychological factors. Non-invasive methods were used to assess subclinical atherosclerosis in two regions of the body, i.e., ankle-brachial blood pressure index (ABI) and coronary artery calcification (CAC). Multivariate logistic regression was used to examine the association between each psychological measure and subclinical atherosclerosis, after the adjustment for socio-demographic factors, sleep quality, young adulthood/early middle age and late-life CVD risk status, and psychological ill-being as appropriate. RESULTS: The burden of major cardiovascular disease risk factors did not significantly differ across tertiles of psychological factors. In multivariate adjusted models, trait anxiety was associated with calcification: those in the second tertile were significantly more likely to have CAC >0 compared to those in the lowest anxiety tertile [OR=1.68; 95% CI=1.09-2.58], but no significant difference was observed for Tertile III of trait anxiety [OR=1.31; 95% CI=0.75-2.27]. No association was seen between psychological measures and ABI. CONCLUSION: Of several psychological factors, only trait anxiety was significantly associated with CAC.