@article {6735, title = {Older Adults, "Malignant" Left Ventricular Hypertrophy, and Associated Cardiac-Specific Biomarker Phenotypes to Identify the Differential Risk of New-Onset Reduced Versus Preserved Ejection Fraction Heart Failure: CHS (Cardiovascular Health Study).}, journal = {JACC Heart Fail}, volume = {3}, year = {2015}, month = {2015 Jun}, pages = {445-55}, abstract = {

OBJECTIVES: This study hypothesized that biomarkers of subclinical myocardial injury (high-sensitivity cardiac troponin T [hs-cTnT]) and hemodynamic stress (N-terminal pro-B-type natriuretic peptide [NT-proBNP]) would differentiate heart~failure (HF) risk among older adults with left ventricular hypertrophy (LVH).

BACKGROUND: The natural history of LVH, an important risk factor for HF, is heterogeneous.

METHODS: NT-proBNP and hs-cTnT were measured at baseline and after 2 to 3 years in older adults without prior HF or myocardial infarction in the CHS (Cardiovascular Health Study). LVH and left ventricular ejection fraction were determined by echocardiography. HF events were adjudicated over a median of 13.1 years and classified as preserved or reduced left ventricular ejection fraction (heart failure with preserved ejection fraction or heart failure with reduced ejection fraction [HFrEF]). Adjusted risk of HF by LVH and biomarker tertiles, and by LVH and longitudinal increase in each biomarker was estimated using Cox regression.

RESULTS: Prevalence of LVH was 12.5\% among 2,347 participants with complete measures. Adjusted risk of HF (N~=~643 events) was approximately 3.8-fold higher among participants with LVH and in the highest biomarker tertile, compared with those with low biomarker levels without LVH (NT-proBNP, hazard ratio [HR]: 3.78; 95\% confidence interval [CI]: 2.78 to 5.15 and hs-cTnT, HR: 3.86; 95\% CI: 2.84 to 5.26). The adjusted risk of HFrEF was 7.8 times higher among those with the highest tertile of hs-cTnT and LVH (HR: 7.83; 95\% CI: 4.43 to 13.83). Those with LVH and longitudinal increases in hs-cTnT or NT-proBNP were approximately 3-fold more likely to develop HF, primarily HFrEF, compared with those without LVH and with stable biomarkers.

CONCLUSIONS: The combination of LVH with greater hs-cTnT or NT-proBNP levels, and their longitudinal increase, identifies older adults at highest risk for symptomatic HF, especially HFrEF. These biomarkers may characterize sub-phenotypes in the transition from LVH to HF and suggest modifiable targets for prevention.

}, keywords = {Aged, Biomarkers, Echocardiography, Female, Heart Failure, Humans, Hypertrophy, Left Ventricular, Male, Natriuretic Peptide, Brain, Peptide Fragments, Phenotype, Prospective Studies, Risk Factors, Troponin T}, issn = {2213-1787}, doi = {10.1016/j.jchf.2014.12.018}, author = {Seliger, Stephen L and de Lemos, James and Neeland, Ian J and Christenson, Robert and Gottdiener, John and Drazner, Mark H and Berry, Jarett and Sorkin, John and DeFilippi, Christopher} } @article {8626, title = {Performance of the Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Body Mass Index.}, journal = {JAMA Netw Open}, volume = {3}, year = {2020}, month = {2020 10 01}, pages = {e2023242}, abstract = {

Importance: Obesity is a global health challenge and a risk factor for atherosclerotic cardiovascular disease (ASVCD). Performance of the pooled cohort equations (PCE) for ASCVD risk by body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) is unknown.

Objective: To assess performance of the PCE across clinical BMI categories.

Design, Setting, and Participants: This cohort study used pooled individual-level data from 8 community-based, prospective, longitudinal cohort studies with 10-year ASCVD event follow-up from 1996 to 2016. We included all adults ages 40 to 79 years without baseline ASCVD or statin use, resulting in a sample size of 37 311 participants. Data were analyzed from August 2017 to July 2020.

Exposures: Participant BMI category: underweight (<18.5), normal weight (18.5 to <25), overweight (25 to <30), mild obesity (30 to <35), and moderate to severe obesity (>=35).

Main Outcomes and Measures: Discrimination (Harrell C statistic) and calibration (Nam-D{\textquoteright}Agostino χ2 goodness-of-fit test) of the PCE across BMI categories. Improvement in discrimination and net reclassification with addition of BMI, waist circumference, and high-sensitivity C-reactive protein (hsCRP) to the PCE.

Results: Among 37 311 participants (mean [SD] age, 58.6 [11.8] years; 21 897 [58.7\%] women), 380 604 person-years of follow-up were conducted. Mean (SD) baseline BMI was 29.0 (6.2), and 360 individuals (1.0\%) were in the underweight category, 9937 individuals (26.6\%) were in the normal weight category, 13 601 individuals (36.4\%) were in the overweight category, 7783 individuals (20.9\%) were in the mild obesity category, and 5630 individuals (15.1\%) were in the moderate to severe obesity category. Median (interquartile range [IQR]) 10-year estimated ASCVD risk was 7.1\% (2.5\%-15.4\%), and 3709 individuals (9.9\%) developed ASCVD over a median (IQR) 10.8 [8.5-12.6] years. The PCE overestimated ASCVD risk in the overall cohort (estimated/observed [E/O] risk ratio, 1.22; 95\% CI, 1.18-1.26) and across all BMI categories except the underweight category. Calibration was better near the clinical decision threshold in all BMI groups but worse among individuals with moderate or severe obesity (E/O risk ratio, 1.36; 95\% CI, 1.25-1.47) and among those with the highest estimated ASCVD risk >=20\%. The PCE C statistic overall was 0.760 (95\% CI, 0.753-0.767), with lower discrimination in the moderate or severe obesity group (C statistic, 0.742; 95\% CI, 0.721-0.763) compared with the normal-range BMI group (C statistic, 0.785; 95\% CI, 0.772-0.798). Waist circumference (hazard ratio, 1.07 per 1-SD increase; 95\% CI, 1.03-1.11) and hsCRP (hazard ratio, 1.07 per 1-SD increase; 95\% CI, 1.05-1.09), but not BMI, were associated with increased ASCVD risk when added to the PCE. However, these factors did not improve model performance (C statistic, 0.760; 95\% CI, 0.753-0.767) with or without added metrics.

Conclusions and Relevance: These findings suggest that the PCE had acceptable model discrimination and were well calibrated at clinical decision thresholds but overestimated risk of ASCVD for individuals in overweight and obese categories, particularly individuals with high estimated risk. Incorporation of the usual clinical measures of obesity did not improve risk estimation of the PCE. Future research is needed to determine whether incorporation of alternative high-risk obesity markers (eg, weight trajectory or measures of visceral or ectopic fat) into the PCE may improve risk prediction.

}, keywords = {Adult, Aged, Body Mass Index, Cardiovascular Diseases, Cohort Studies, Correlation of Data, Female, Humans, Longitudinal Studies, Male, Middle Aged, Proportional Hazards Models, Prospective Studies, Risk Assessment, Risk Factors}, issn = {2574-3805}, doi = {10.1001/jamanetworkopen.2020.23242}, author = {Khera, Rohan and Pandey, Ambarish and Ayers, Colby R and Carnethon, Mercedes R and Greenland, Philip and Ndumele, Chiadi E and Nambi, Vijay and Seliger, Stephen L and Chaves, Paulo H M and Safford, Monika M and Cushman, Mary and Xanthakis, Vanessa and Vasan, Ramachandran S and Mentz, Robert J and Correa, Adolfo and Lloyd-Jones, Donald M and Berry, Jarett D and de Lemos, James A and Neeland, Ian J} } @article {8976, title = {Diabetes Status Modifies the Association Between Different Measures of Obesity and Heart Failure Risk Among Older Adults: A Pooled Analysis of Community-Based NHLBI Cohorts.}, journal = {Circulation}, volume = {145}, year = {2022}, month = {2022 01 25}, pages = {268-278}, abstract = {

BACKGROUND: Obesity and diabetes are associated with a higher risk of heart failure (HF). The interrelationships between different measures of adiposity-overall obesity, central obesity, fat mass (FM)-and diabetes status for HF risk are not well-established.

METHODS: Participant-level data from the ARIC study (Atherosclerosis Risk in Communities; visit 5) and the CHS (Cardiovascular Health Study; visit 1) cohorts were obtained from the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center, harmonized, and pooled for the present analysis, excluding individuals with prevalent HF. FM was estimated in all participants using established anthropometric prediction equations additionally validated using the bioelectrical impedance-based FM in the ARIC subgroup. Incident HF events on follow-up were captured across both cohorts using similar adjudication methods. Multivariable-adjusted Fine-Gray models were created to evaluate the associations of body mass index (BMI), waist circumference (WC), and FM with risk of HF in the overall cohort as well as among those with versus without diabetes at baseline. The population attributable risk of overall obesity (BMI>=30 kg/m), abdominal obesity (WC>88 and 102 cm in women and men, respectively), and high FM (above sex-specific median) for incident HF was evaluated among participants with and without diabetes.

RESULTS: The study included 10 387 participants (52.9\% ARIC; 25.1\% diabetes; median age, 74 years). The correlation between predicted and bioelectrical impedance-based FM was high (=0.90; n=5038). During a 5-year follow-up, 447 participants developed HF (4.3\%). Higher levels of each adiposity measure were significantly associated with higher HF risk (hazard ratio [95\% CI] per 1 SD higher BMI=1.15 [1.05, 1.27], WC=1.22 [1.10, 1.36]; FM=1.13 [1.02, 1.25]). A significant interaction was noted between diabetes status and measures of BMI ( interaction=0.04) and WC ( interaction=0.004) for the risk of HF. In stratified analysis, higher measures of each adiposity parameter were significantly associated with higher HF risk in individuals with diabetes (hazard ratio [95\% CI] per 1 SD higher BMI=1.29 [1.14-1.47]; WC=1.48 [1.29-1.70]; FM=1.25 [1.09-1.43]) but not those without diabetes, including participants with prediabetes and euglycemia. The population attributable risk percentage of overall obesity, abdominal obesity, and high FM for incident HF was higher among participants with diabetes (12.8\%, 29.9\%, and 13.7\%, respectively) versus those without diabetes (<=1\% for each).

CONCLUSIONS: Higher BMI, WC, and FM are strongly associated with greater risk of HF among older adults, particularly among those with prevalent diabetes.

}, keywords = {Aged, Cohort Studies, Diabetes Mellitus, Female, Heart Failure, Humans, Male, National Heart, Lung, and Blood Institute (U.S.), Obesity, Risk Factors, United States}, issn = {1524-4539}, doi = {10.1161/CIRCULATIONAHA.121.055830}, author = {Patel, Kershaw V and Segar, Matthew W and Lavie, Carl J and Kondamudi, Nitin and Neeland, Ian J and Almandoz, Jaime P and Martin, Corby K and Carbone, Salvatore and Butler, Javed and Powell-Wiley, Tiffany M and Pandey, Ambarish} } @article {9637, title = {Development and Validation of the American Heart Association{\textquoteright}s PREVENT Equations.}, journal = {Circulation}, volume = {149}, year = {2024}, month = {2024 Feb 06}, pages = {430-449}, abstract = {

BACKGROUND: Multivariable equations are recommended by primary prevention guidelines to assess absolute risk of cardiovascular disease (CVD). However, current equations have several limitations. Therefore, we developed and validated the American Heart Association Predicting Risk of CVD EVENTs (PREVENT) equations among US adults 30 to 79 years of age without known CVD.

METHODS: The derivation sample included individual-level participant data from 25 data sets (N=3 281 919) between 1992 and 2017. The primary outcome was CVD (atherosclerotic CVD and heart failure). Predictors included traditional risk factors (smoking status, systolic blood pressure, cholesterol, antihypertensive or statin use, and diabetes) and estimated glomerular filtration rate. Models were sex-specific, race-free, developed on the age scale, and adjusted for competing risk of non-CVD death. Analyses were conducted in each data set and meta-analyzed. Discrimination was assessed using the Harrell C-statistic. Calibration was calculated as the slope of the observed versus predicted risk by decile. Additional equations to predict each CVD subtype (atherosclerotic CVD and heart failure) and include optional predictors (urine albumin-to-creatinine ratio and hemoglobin A1c), and social deprivation index were also developed. External validation was performed in 3 330 085 participants from 21 additional data sets.

RESULTS: Among 6 612 004 adults included, mean{\textpm}SD age was 53{\textpm}12 years, and 56\% were women. Over a mean{\textpm}SD follow-up of 4.8{\textpm}3.1 years, there were 211 515 incident total CVD events. The median C-statistics in external validation for CVD were 0.794 (interquartile interval, 0.763-0.809) in female and 0.757 (0.727-0.778) in male participants. The calibration slopes were 1.03 (interquartile interval, 0.81-1.16) and 0.94 (0.81-1.13) among female and male participants, respectively. Similar estimates for discrimination and calibration were observed for atherosclerotic CVD- and heart failure-specific models. The improvement in discrimination was small but statistically significant when urine albumin-to-creatinine ratio, hemoglobin A1c, and social deprivation index were added together to the base model to total CVD (ΔC-statistic [interquartile interval] 0.004 [0.004-0.005] and 0.005 [0.004-0.007] among female and male participants, respectively). Calibration improved significantly when the urine albumin-to-creatinine ratio was added to the base model among those with marked albuminuria (>300 mg/g; 1.05 [0.84-1.20] versus 1.39 [1.14-1.65]; =0.01).

CONCLUSIONS: PREVENT equations accurately and precisely predicted risk for incident CVD and CVD subtypes in a large, diverse, and contemporary sample of US adults by using routinely available clinical variables.

}, keywords = {Adult, Aged, Albumins, American Heart Association, Atherosclerosis, Cardiovascular Diseases, Creatinine, Female, Glycated Hemoglobin, Heart Failure, Humans, Male, Middle Aged, Risk Assessment, Risk Factors}, issn = {1524-4539}, doi = {10.1161/CIRCULATIONAHA.123.067626}, author = {Khan, Sadiya S and Matsushita, Kunihiro and Sang, Yingying and Ballew, Shoshana H and Grams, Morgan E and Surapaneni, Aditya and Blaha, Michael J and Carson, April P and Chang, Alexander R and Ciemins, Elizabeth and Go, Alan S and Gutierrez, Orlando M and Hwang, Shih-Jen and Jassal, Simerjot K and Kovesdy, Csaba P and Lloyd-Jones, Donald M and Shlipak, Michael G and Palaniappan, Latha P and Sperling, Laurence and Virani, Salim S and Tuttle, Katherine and Neeland, Ian J and Chow, Sheryl L and Rangaswami, Janani and Pencina, Michael J and Ndumele, Chiadi E and Coresh, Josef} }