Author + information
- Received July 3, 2018
- Accepted July 24, 2018
- Published online November 26, 2018.
- Vishal N. Rao, MD, MPHa,b,
- Di Zhao, PhDc,
- Matthew A. Allison, MD, MPHd,
- Eliseo Guallar, MD, DrPHc,
- Kavita Sharma, MDa,
- Michael H. Criqui, MD, MPHd,
- Mary Cushman, MDe,
- Roger S. Blumenthal, MDa and
- Erin D. Michos, MD, MHSa,c,∗ ()
- aCiccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins School of Medicine, Baltimore, Maryland
- bDivision of Cardiology, Duke University School of Medicine, Durham, North Carolina
- cDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- dDivision of Preventive Medicine, University of California San Diego, La Jolla, California
- eDivision of Hematology, University of Vermont, Burlington, Vermont
- ↵∗Address for correspondence:
Dr. Erin D. Michos, Division of Cardiology, Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, Maryland 21287.
Objectives This study sought to compare various measures of adiposity with risk for incident hospitalized heart failure (HF) with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF).
Background Obesity is a risk factor for HF, particularly HFpEF. It is unknown which measures of adiposity, including anthropometrics and computed tomography (CT)-measured fat area, are most predictive of HF subtypes.
Methods The authors studied 1,806 participants of the MESA (Multi-Ethnic Study of Atherosclerosis) study without baseline cardiovascular disease who underwent anthropometrics (body mass index [BMI] and waist circumference) and an abdominal CT. Subcutaneous and visceral adipose tissue (VAT) were measured from a single CT slice at L2-L3. Cox hazard models were used to examine associations of adiposity with incident hospitalized HFpEF and HFrEF events. Fully adjusted models included demographics, HF risk factors, and N-terminal pro-B-type natriuretic peptide.
Results Over a mean follow-up of 11 years, there were 34 HFpEF and 36 HFrEF events. The fully adjusted hazard ratio (95% confidence interval [CI]) per 1-SD higher of each anthropometric and CT-measured adiposity measures for incident HFpEF were as follows: BMI HR: 1.66; 95% CI: 1.12 to 2.45; waist circumference HR: 1.59; 95% CI: 1.05 to 2.40; and VAT HR: 2.24; 95% CI: 1.44 to 3.49. None of these adiposity measures were associated with HFrEF. Even among overweight/obese adults (BMI ≥25 kg/m2), assessment of VAT (per 1-SD) was strongly associated with HFpEF (HR: 2.78; 95% CI: 1.62 to 4.76). Subcutaneous adipose tissue was neither associated with HFpEF nor HFrEF.
Conclusions In a multiethnic cohort free of cardiovascular disease, CT-measured VAT was independently associated with incident hospitalized HFpEF but not HFrEF. Measuring visceral fat at the time of CT imaging for other indications may offer additional prognostication of HF risk. (Multi-Ethnic Study of Atherosclerosis [MESA]; NCT00005487)
Obesity is a stronger risk factor for heart failure (HF) than for other types of cardiovascular diseases (CVD), an association not fully explained by obesity-related cardiometabolic risk factors (1). HF with preserved ejection fraction (HFpEF) has increased significantly over the past decade and now accounts for ∼50% of all HF cases (2,3). Obesity, and in particular central adiposity, directly correlates with increasing left ventricular (LV) stiffness, contributing to the diastolic dysfunction in HFpEF (4,5). Moreover, in some studies, obesity defined by body mass index (BMI) has been associated with a greater risk for HFpEF, but not for HF with reduced ejection fraction (HFrEF) (3,6). The risk that obesity confers on HFpEF may differ by sex and race/ethnicity (i.e., it is stronger among African-American women) (7).
Visceral adipose tissue (VAT), which is stored in the abdominal cavity and accounts for approximately 20% of adipose tissue, is pro-inflammatory and increases cardiovascular risk by promoting metabolic diseases such as diabetes, dyslipidemia, and hypertension (8). VAT appears to have different associations with cardiometabolic risk than adipose tissue residing in other compartments, such as subcutaneous adipose tissue (SAT) (9). Among obese people with coronary artery disease, it appears that the distribution of fat, rather than BMI itself, is more directly associated with mortality (10).
Although obesity is a well-established risk factor for HFpEF (3,7), it is unclear which of the various anthropometric measures of adiposity (i.e., BMI, weight-to-hip ratio [WHR], or waist circumference [WC]) is most predictive of HFpEF. It is also unknown whether directly measured adipose tissue derived from computed tomography (CT) scans is more predictive of HFpEF risk than anthropometric data, particularly among those considered normal weight by traditional BMI measures. Additionally, obesity-related biomarkers such as adipokines also have prognostic value in HF risk (11–13), but it is unknown if these biomarkers better predict HF than anthropometrics and CT-measured adiposity.
Thus, the purpose of this study was to compare the association of anthropometric measures of adiposity (BMI, WHR, and WC), CT-derived adiposity measures (VAT, SAT), and obesity-related biomarkers with incident hospitalized HFpEF (and compared to HFrEF) in a multi-ethnic cohort.
Design and study participants
The MESA (Multi-Ethnic Study of Atherosclerosis) investigation is a multicenter cohort reviewing risk factors for and clinical implications of subclinical CVD (14). The study enrolled 6,814 White, Black, Hispanic, and Chinese-American men and women between the ages of 45 and 84 years old who were free of clinical CVD and HF at enrollment. Participants were enrolled from six different U.S. sites: New York, New York; Baltimore, Maryland; Chicago, Illinois; Los Angeles, California; St. Paul, Minnesota; and Winston-Salem, North Carolina (14). Visit 1 (enrollment) took place between 2000 and 2002, visit 2 between 2002 and 2004, visit 3 between 2004 and 2005, visit 4 between 2005 and 2007, and visit 5 between 2010 and 2012.
A random subset of MESA participants (n = 1,970) underwent abdominal CT scans at either visit 2 or visit 3 (randomly assigned) to measure abdominal aortic calcification as previously described (15). Among these, 1,947 had visualization of abdominal cavity on the CT that was retrospectively reviewed for body composition (16). For our analyses, we excluded those with a HF event before the abdominal CT scan date (n = 18), missing subcutaneous fat and visceral fat for all slices (n = 104), missing ejection fraction at time of HF diagnosis (n = 4), or missing other covariates in our main adjusted model (n = 15). Thus, we included a total of 1,806 participants in our sample who had both CT-derived adiposity measurements and anthropometrics.
At each MESA visit, demographics, medical history, physical examination, and medication use were obtained for each participant as previously described (14). Visit 2 or visit 3, the time of the participant’s abdominal CT, was considered their baseline for this present analysis. The MESA study and the abdominal CT ancillary study were approved by the Institutional Review Board at each participating site, and informed consent was obtained from each participant.
Measures of adiposity
Anthropometric measures of weight, height, WC, and hip circumference were measured at each visit; each marker was measured twice using a standardized protocol and averaged (14). Weight was measured to the nearest 0.5 lb. Height was measured using a vertical scale to the nearest 0.5 cm. WC was measured at the level of the minimum abdominal circumference to the nearest 0.1 cm. Hip circumference was measured at the level of the maximum girth at the pubic symphysis to the nearest 0.1 cm. WHR was calculated from waist and hip circumference measurements. BMI was calculated as the ratio of weight to height squared (kg/m2). For this analysis, we used the anthropometric obtained at the same visit as their CT scan (visit 2 or visit 3).
Visceral and subcutaneous fat were measured from scans obtained using the Imatron C-150 electron-beam (Imatron Inc., San Francisco, California), Siemens S4+ Volume Zoom (Siemens, Erlangen, Germany), or General Electric Hi Speed LX CT (General Electric Medical Systems, Waukesha, Wisconsin) scanners. We defined VAT as the total adipose tissue enclosed within the abdominal cavity and SAT as the total adipose tissue outside of the abdominal cavity but not within muscle tissue. For this study, participants had 6 slices obtained from L2 to L5 vertebral spaces (i.e., 2 at L2-L3, 2 at L3-L4, and 2 at L4-L5) interrogated for adipose tissue measurements (cm3). Two analysts independently evaluated each CT using the Medical Imaging Processing Analysis and Visualization Software MIPAV version 4.1.2 (National Institutes of Health, Bethesda, Maryland). Inter-rater and intrarater reliability for the different abdominal CT measurements ranged from 0.92 to 0.99. For our primary analysis, VAT and SAT were defined using the average of 2 slices obtained at L2-L3 and adjusted for height, as has been done previously (17). In a sensitivity analysis, we also included the sum of all 6 slices for those participants who were not missing any slices.
As previously reported (16,18), the obesity-related adipokines (adiponectin, leptin, and resistin), were measured from stored (fasting) blood from the CT visit (visit 2 or visit 3) using a Bio-Rad Luminex flow cytometry (Millepore, Billerica, Massachusetts) at the Laboratory for Clinical Biochemistry Research (University of Vermont, Burlington, Vermont). The coefficients of variation ranged from 6% to 13%. Insulin was measured by radioimmunoassay using the Linco Human Insulin Specific assay (Linco Research, Inc., St. Charles, Missouri), with a coefficient of variation of 4.9%. The adipokine and insulin biomarkers had a skewed distribution and were log-transformed for all analyses.
Using data from their respective CT visit (visit 2 or visit 3), we considered demographics and socioeconomic factors (age, sex, race/ethnicity, and study site), behavioral factors (smoking status and physical activity), systolic blood pressure (BP), use of antihypertensive medications, diabetes, total cholesterol (mg/dl), high-density lipoprotein cholesterol (mg/dl), use of lipid lowering medications, estimated glomerular filtration rate (eGFR), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) (pg/ml) for covariate adjustment. Physical activity was determined using a 28-item Typical Week Physical Activity Survey and measured in metabolic equivalent minutes per week (16). Resting BP was measured 3 times in the seated position, with the average of the last 2 measurements used. Diabetes was defined as a fasting blood glucose ≥126 mg/dl and/or the self-reported history of a physician-diagnosis of diabetes, or the use of diabetes medications. Renal function was measured during visit 1 and 3; visit 1 eGFR was used for those who had a CT at visit 2. NT-proBNP was measured at visit 1 and in a subset at visit 3; visit 1 values were used for those who had a CT at visit 2 and for those with a CT at visit 3 who were missing visit 3 NT-proBNP measurements.
The primary outcome of interest was incident hospitalization for HFpEF. As a secondary endpoint, we also reported on HFrEF events for comparison. Study participants were followed up from baseline (either visit 2 or 3) until death or until December 31, 2015. Every 9 to 12 months, trained staff contacted participants by telephone to obtain information on hospitalizations. Medical records were reviewed and diagnoses of HF events while hospitalized were adjudicated by a panel of MESA physicians using standardized criteria. We considered probable or definite hospitalized HF events. Probable HF was defined as a physician diagnosis and HF medical treatment. Definite HF required an additional objective criterion such as evidence of pulmonary congestion on chest radiography, reduced left ventricular (LV) function by echocardiography or ventriculography, or evidence of LV diastolic dysfunction. HFpEF was defined as a HF event with an ejection fraction ≥45% as identified on echocardiogram or imaging studies at the time of HF hospitalization. HFrEF was a HF event with an ejection fraction of <45%.
Baseline characteristics between participants who developed HFpEF and those who did not were compared using a 2-sided Student’s t-test, Wilcoxon’s rank-sum test, or chi-square test, when appropriate. The exposures of interest examined were the anthropometric measures, the CT-derived measures, and the obesity-related biomarkers. Multivariable-adjusted Cox proportional hazard regression models were used to estimate hazard ratios (HRs) and their 95% confidence intervals (CIs) between the various adiposity measures (per 1-SD increment for each adiposity marker) with risk of incident HF, HFpEF, or HFrEF. To determine whether VAT provided additional prognostic information over BMI, we assessed the risk of each HF outcome per 1-SD higher VAT, stratified by BMI categories.
We examined 3 progressively adjusted traditional Cox models. In the initial model, we adjusted for the demographic and behavioral factors of age, sex, race/ethnicity, smoking status, and physical activity. A second model, which serves as our primary model (model 2), further adjusted for CVD/HF risk factors (systolic BP, use of antihypertensive medications, total cholesterol, high-density lipoprotein cholesterol, use of lipid lowering medications, and diabetes) and eGFR. A third model further adjusted for NT-proBNP, which may serve as a marker of subclinical HF. Additionally, because outcome events were few in this subcohort and proportional hazard assumption may not hold, we performed sensitivity analyses in which we used a stratified Cox model approach (instead of the traditional Cox model) to avoid over-fitting the model with too many covariates (19).
Effect modifications by sex were tested by including an interaction term between adiposity markers and sex. All statistical analyses were performed using Stata 14 (StataCorp LP, College Station, Texas).
Baseline characteristics by incident HF status
The overall baseline characteristics of the 1,806 participants included in this analysis are shown in Table 1. The mean age was 64.5 years, 52% were women, and 40% were White, 14% Chinese-American, 21% Black, and 26% Hispanic-American. Seventy participants (3.9%) developed HF over a mean follow-up time of 10.5 ± 2.9 years). Of these HF events, 34 were HFpEF and 36 were HFrEF.
Table 1 also shows the baseline characteristics of those who developed incident hospitalized HFpEF during follow-up versus the characteristics of those who did not. Participants who developed HFpEF were more likely to be older, have higher systolic BP and use of antihypertensive medications, have a greater prevalence of diabetes, have a lower eGFR, and have a higher NT-proBNP. With respect to anthropometric measures, those with incident hospitalized HFpEF had higher baseline BMI, WHR, and WC (p < 0.05 for all). Average total VAT was also higher among those with incident hospitalized HFpEF versus those who did not (231 cm3 vs. 163 cm3; p < 0.001), but total SAT did not differ between the 2 groups.
The baseline characteristics stratified by incident HFrEF status are shown in Online Table 1. There was no difference in adiposity measures at baseline between those with and without incident HFrEF.
Measures of adiposity and HF events
The HRs (95% CI) associated with risk of incident hospitalized HFpEF (34 events) by adiposity measures are shown in Table 2. After adjusting for demographic and behavioral factors (model 1), the adiposity measures of BMI, WHR, WC, VAT, leptin, and insulin were all significantly associated with incident HFpEF. However, in our primary model adjusted for CVD risk factors (model 2), only BMI, WC, and VAT remained statistically significantly associated with HFpEF with HRs per 1-SD higher adiposity measures as follows: BMI HR: 1.57; 95% CI: 1.08 to 2.27; WC HR: 1.61; 95% CI: 1.09 to 2.38; and VAT (single CT-slice) HR: 1.94; 95% CI: 1.29 to 2.91. These adiposity measures remained statistically significantly associated with HFpEF after further adjusting for NT-proBNP (model 3). The obesity-related laboratory markers were not associated with increased HFpEF risk in our primary model. Notably, SAT was not associated with HFpEF risk in any model.
The HRs (95% CI) associated with risk of incident HFrEF (36 events) by adiposity measures are shown in Table 3. There were no statistically significant associations of the anthropometric and CT adiposity measures or the obesity-related laboratory markers with HFrEF events in any model.
Online Table 2 shows the associations of these adiposity measures (per 1-SD) with all HF events combined (n = 70). In our primary model adjusted for CVD risk factors (model 2), only VAT was associated with combined HF risk with HR of VAT (single slice): 1.35; 95% CI: 1.02 to 1.79. However, after further adjustment for NT-proBNP (model 3), BMI, WC, and VAT were all associated with HF, similar to their associations with HFpEF.
Table 4 shows the HRs (95% CI) of VAT (per 1-SD increment) stratified by BMI categories. Among participants who were obese or overweight (BMI ≥25 kg/m2), greater VAT was predictive of any HF (HR: 1.66; 95% CI: 1.18 to 2.33) and HFpEF (HR: 2.37; 95% CI: 1.44 to 3.89), but not HFrEF (HR: 1.17; 95% CI: 0.72 to 1.91) in our primary adjusted model. There was a similar trend for greater HFpEF risk with increasing VAT among those with normal BMI (<25 kg/m2) but this was not statistically significant with wide confidence intervals (HR: 1.21; 95% CI: 0.28 to 5.16) given few HFpEF events (n = 12) among those with normal BMI. Findings were similar after further adjustment for NT-proBNP in model 3.
Findings for the aforementioned analyses were similar in sensitivity analysis when VAT and SAT were defined using the total of all 6 abdominal slices, rather than a single slice at L2-L3 (also shown in Tables 2, 3 and 4). Findings were also generally similar using a stratified Cox model (with limited covariates included to avoid over-fitting, as shown in Online Table 3) instead of a traditional Cox proportional model. In this stratified model, BMI, WC, and VAT remained statistically significantly associated with incident hospitalized HFpEF, but WHR, leptin, insulin, and resistin were also statistically significantly associated with HFpEF as well.
There was no meaningful interaction by sex for all analyses examined, although the power to detect interactions was limited by few events.
In this longitudinal, multiethnic cohort study, we compared anthropometric-measured adiposity, CT-measured adiposity, adiposity-related adipokines, and insulin with their risk for incident hospitalized HF. The adiposity measures of BMI, WC, and VAT were all associated with incident hospitalized HFpEF risk, but not HFrEF, in our models adjusted for demographic, lifestyle, and CVD risk factors. These associations remained statistically significant after further adjusting for NT-proBNP, which may serve as a subclinical (intermediate) marker for HF. The association of VAT with incident hospitalized HFpEF was qualitatively stronger than the associations attributed to the anthropometric measures; however, CIs for these adiposity measures overlapped. In contrast to anthropometrics and VAT, the adiposity-related laboratory measures and SAT were not independently associated with either HFpEF or HFrEF risk.
Consistent with previously described cohorts (2,3), nearly one-half of our study population who developed HF had HFpEF. Among people who are overweight or obese (BMI ≥25 kg/m2), higher VAT levels provided additional value in predicting HFpEF events. Furthermore, VAT was strongly associated with HFpEF risk, whereas SAT had no association. Thus, our work confirms that the distribution of adiposity is relevant to HFpEF risk. This relationship may provide clues into the pathophysiology of obesity and HFpEF, and expand on prior literature describing the association of visceral fat and cardiovascular risk factors, including diabetes, hypertension, and dyslipidemia (8,20) and the HF-related biomarker of NT-proBNP (21).
Whereas it is unlikely that abdominal CT will ever replace anthropometrics (i.e., BMI, WC, and WHR) for routine screening due to cost and radiation exposure, many individuals undergo CT scanning for other indications. Quantitative assessment of VAT at the time of CT scanning for other purposes may further identify people who are at increased risk of HFpEF who might benefit from more aggressive preventive lifestyle interventions.
Prior studies have shown that BMI is a risk factor for HF (1), yet few investigated differences in HF subtypes (3,6). Similar to our findings, several other studies have found that obesity, measured by BMI, was associated with HFpEF but not HFrEF (3,7). In contrast, a Dutch cohort showed equal hazards of both incident HFpEF and HFrEF by BMI (22). Our study compared several measures of adiposity, both by anthropometrics and CT-measured, with the goal of determining if 1 adiposity measure was more strongly predictive of HFpEF than the others. However, we found that the various anthropometric measures and VAT generally had similar magnitudes of associations for HFpEF, but none were predictive of HFrEF. Despite the risk that BMI has on some types of incident HF, other studies have noted a paradoxical relationship between higher BMI and all-cause mortality among people with established HF and particularly with HFrEF (23,24)—the so called “obesity-paradox.” Contrary to this trend, following diagnosis of HFpEF, mortality appears to be higher among those who are obese (25,26). Investigating whether changes in visceral adiposity after HF diagnosis affects long-term outcomes may clarify the protective role obesity confers in those with HFrEF diagnosis and provide insight into disease progression in those with HFpEF.
The distribution of adipose tissue affects mortality risk (10), and unlike VAT, prior work has found the relationship of SAT and diabetes to be inversely related among women and no association among men (27). The present study found that SAT is not predictive of any HF events, which is consistent with SAT associations with other subclinical and clinical CVD (9).
Among measured hormones, our primary analysis (fully adjusted for CVD risk factors including diabetes) did not show an independent association between serum adipokines or insulin and incident HFpEF. However, in our stratified Cox models with more conservative (limited) covariate adjustment (Online Table 3), leptin, resistin, and insulin were all strongly associated with HFpEF. Rising insulin levels may serve as an intermediate marker of the insulin resistance in diabetes, a known risk factor for HFpEF, which is why associations perhaps no longer remained significant after adjusting for diabetes status. Leptin levels are known to be positively, and adiponectin levels are inversely, associated with BMI (28). In this limited stratified analysis, higher leptin levels were predictive of HFpEF, consistent with previous work showing leptin levels to correlate with diastolic dysfunction (29). Adiponectin, however, was not associated with incident HFpEF. Also in the limited stratified model, resistin, which is derived from adipocytes and associated with inflammation, was associated with incident HFpEF. This is consistent with a prior MESA study which found that resistin was predictive of incident CVD, coronary heart disease, and all HF (30). In sum, biomarkers associated with HFpEF appear to differ from those associated with HFrEF (31).
Study strengths and limitations
Our study has many strengths including the comparison of several measures of anthropometric-derived adiposity, CT-derived adiposity, and adiposity-related laboratory markers among men and women free of CVD and HF at baseline from a multiethnic cohort, who were followed for long-term HF events (adjudicated by an expert panel and further subclassified as either both HFpEF and HFrEF). Our study provides further insight into the relative contributions of various adiposity markers to HF risk and its subtype, which may guide further work in this area.
Nonetheless, our study results should be interpreted in the context of the following limitations. First, reported HF events were adjudicated hospitalized HF cases, so milder HF cases identified and treated as outpatients were missed. Second, only 34 individuals of our subcohort developed incident hospitalized HFpEF and 36 developed HFrEF, so we were underpowered to compare model prediction among the various adiposity measures or to conclusively examine for sex or race/ethnicity interactions. We used a traditional Cox model which has potential risk for over-fitting in the setting of few events. To address this, we performed sensitivity analyses using a stratified Cox model to avoid the potential of violation of proportional hazard assumptions. Results were consistent among adiposity and CT-derived measures, but not serum markers, which showed some significant relationships not seen in the traditional Cox model. Third, our study was observational and, although we adjusted for numerous potential confounding lifestyle variables, residual confounding may be present in these analyses. Finally, we compared multiple measures of adiposity and results may be statistically significant by chance, although results were internally consistent among adiposity measures.
In a large, multiethnic U.S. cohort free of CVD, we show that the anthropometric measures of BMI and WC, and the CT-measure of VAT were all strong risk factors for incident hospitalized HFpEF but not HFrEF. Subcutaneous fat was not predictive of either HF subtype. Although our study was observational and cannot determine causation, our findings lend support to the potential causal role of visceral fat in the pathogenesis of a phenotype of HFpEF. Future research is warranted to understand the best use of visceral adiposity imaging to identify individuals at high risk of developing HF and best strategies to reduce this risk.
COMPETENCY IN MEDICAL KNOWLEDGE: Among adults free of CVD and HF, both anthropometrics and CT-measured VAT are associated with increased risk for subsequent development of HFpEF, which emphasizes the importance of lifestyle modification and weight management for HFpEF prevention. Among adiposity-related biomarkers, leptin, resistin, and adiponectin were not associated with HFpEF in our primary analysis fully adjusted for CVD risk factors. No adiposity measure was associated with HFrEF. Whereas VAT was associated with HFpEF, SAT was not, suggesting that the distribution of body fat matters for HF risk. For those undergoing CT for another indication, the extent of CT-assessed VAT may provide independent prognostic information about HFpEF risk even among those already diagnosed as being overweight/obese by BMI.
TRANSLATIONAL OUTLOOK: Results from this observational study provide insight into the relationship of different measures of adiposity (anthropometrics vs. CT-measured vs. obesity-related biomarkers) and found differing relationships of these measures for HF and its subtypes of HFpEF and HFrEF. These results might help identify adults at increased risk for HFpEF, possibly inform future screening protocols, and monitor the impact of lifestyle interventions beyond traditional BMI measures for HFpEF prevention.
The authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.
Drs. Michos and Zhao are supported by the Blumenthal Scholars Fund at Johns Hopkins for Preventive Cardiology Research. This research was also supported by NIH grant R01 HL088451. The MESA study is supported by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the NIH/NHLBI; by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from NCATS. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Abbreviations and Acronyms
- body mass index
- blood pressure
- computed tomography
- cardiovascular disease
- estimated glomerular filtration rate
- heart failure
- heart failure with preserved ejection fraction
- heart failure with reduced ejection fraction
- N-terminal pro-B-type natriuretic peptide
- subcutaneous adipose tissue
- visceral adipose tissue
- waist circumference
- waist hip ratio
- Received July 3, 2018.
- Accepted July 24, 2018.
- 2018 American College of Cardiology Foundation
- Ndumele C.E.,
- Matsushita K.,
- Lazo M.,
- et al.
- Sharma K.,
- Kass D.A.
- Ho J.E.,
- Lyass A.,
- Lee D.S.,
- et al.
- Bello N.A.,
- Cheng S.,
- Claggett B.,
- et al.
- Wohlfahrt P.,
- Redfield M.M.,
- Lopez-Jimenez F.,
- et al.
- Pandey A.,
- LaMonte M.,
- Klein L.,
- et al.
- Eaton C.B.,
- Pettinger M.,
- Rossouw J.,
- et al.
- Bays H.E.
- Coutinho T.,
- Goel K.,
- Correa de Sa D.,
- et al.
- Butler J.,
- Kalogeropoulos A.,
- Georgiopoulou V.,
- et al.
- Tanaka K.,
- Wilson R.M.,
- Essick E.E.,
- et al.
- Forbang N.I.,
- McClelland R.L.,
- Remigio-Baker R.A.,
- et al.
- Vella C.A.,
- Allison M.A.,
- Cushman M.,
- et al.
- Christoph M.J.,
- Allison M.A.,
- Pankow J.S.,
- et al.
- Kleinbaum D.G.
- Nazare J.A.,
- Smith J.D.,
- Borel A.L.,
- et al.
- Tsujimoto T.,
- Kajio H.
- Lavie C.J.,
- Milani R.V.,
- Ventura H.O.
- Matsubara M.,
- Maruoka S.,
- Katayose S.
- Fontes-Carvalho R.,
- Pimenta J.,
- Bettencourt P.,
- Leite-Moreira A.,
- Azevedo A.