Abstract
While several studies have reported a positive association between overall adiposity and heart failure (HF) risk, limited and inconsistent data are available on the relation between central adiposity and incident heart failure in older adults. We sought to examine the association between waist circumference and incident heart failure and assess whether sex modifies the relation between waist circumference and heart failure. Prospective study using data on 4861 participants of the Cardiovascular Health Study (1989 to 2007). Heart failure was adjudicated by a committee using information from medical records and medications. We used Cox proportional hazard models to compute hazard ratio. The mean age was 73.0 y for men and 72.3 y for women; 42.5% were men and 15.3% were African-Americans. Waist circumference was positively associated with an increased risk of HF: each standard deviation of waist circumference was associated with a 14% increased risk of HF (95% CI: 3% to 26%) in a multivariable model. There was not a statistically significant sex-by-waist circumference interaction (p=0.081). Body mass index was positively associated with incident HF [HR: 1.22 (95% CI: 1.15–1.29) per standard deviation increase of body mass index], however, this association was attenuated and became non-statistically significant upon additional adjustment for waist circumference [HR: 1.09 (95% CI: 0.99–1.21)]. In conclusion, a higher waist circumference is associated with an increased risk of heart failure independent of body mass index in community-living older men and women.
Keywords: Epidemiology, heart failure, adiposity, risk factors
Introduction
Heart failure (HF) is a major public health concern(1), affecting approximately 4.9 million Americans in 2005, with associated costs of $27.9 billion. At age 40, the lifetime risk of HF is about 20% for men and women(2;3). Despite progress in medical treatment, HF mortality remains high(4;5), ranging from 20% to 50%(6–9). The highest burden of HF, however, falls on adults aged 65 and older, for whom it is the leading cause of hospitalization(10) (11). Older adults are also the fastest growing demographic group, which is bound to amplify the scope of problem.
A large proportion of HF cases is accounted for by antecedent coronary heart disease (CHD), type 2 diabetes, and hypertension(12–16), suggesting that predictors of coronary disease and hypertension might influence the risk of HF. To this end, adiposity is a known risk factor for CHD, hypertension, and type 2 diabetes (3 major risk factors of HF). Currently, 66% of US adults are overweight or obese and this proportion is expected to reach 75% by 2015(17). While several studies have reported a positive association between overall adiposity (such as body mass index -- BMI) or central adiposity and HF risk(18–20) in young adults, limited data are available on the association between central adiposity (i.e. measured by waist circumference –WC) and HF risk in older adults. Older adults tend to have more fat mass for a given BMI than younger adults(21) and the distribution of fat changes with aging (more fat accumulation in abdomen and less fat in extremities)(22).
The Heart and Soul Study(23) reported a 30% increased risk of HF per each standard deviation of waist-hip ratio in people with stable coronary disease while the Health ABC(24) reported a positive association with measures of central adiposity and HF. None of those two studies(23;24) of older adults found an independent association between BMI and HF. While some studies have reported an inverse relation between hip circumference and risk factors for HF, no previous study has examined such relation in older adults. Additional gap in the literature include the absence of data on body composition and heart failure subtypes (normal vs. lower left ventricular ejection fraction - LVEF) and a potential effect modification by sex. Due to body fat redistribution with aging, the relation between adiposity and HF may become weaker at older ages(25), whereas other researchers have reported an association between measures of central adiposity in men but not women(24). This suggests that sex may modify the relation between adiposity and heart failure.
An earlier evaluation of predictors of HF in the Cardiovascular Health Study examined only height and weight, but did not address measures of central adiposity(15).The main purpose of this paper was to test the hypothesis that central adiposity as measured by WC is positively associated with incident HF independent of BMI and that sex modifies the WC-HF relation. In a secondary analysis, we examined the association between a) hip circumference, waist-hip ratio and HF and b) WC and HF with and without normal LVEF.
Methods and procedures
Study population
Study participants were drawn from the Cardiovascular Health Study (CHS), a prospective cohort consisting of 5,888 men and women aged 65 years and older that were randomly selected from Medicare-eligibility lists in four US communities (Forsyth County, NC; Washington County, MD; Sacramento County, CA; and Pittsburgh, PA). A detailed description of methods and procedures in the CHS has been published elsewhere(26). Briefly, participants were not institutionalized or wheelchair-dependent, did not require a proxy for consent, were not receiving treatment for cancer and were expected to remain in their respective regions for 3 years. Between 1989 and 1990, a total of 5,201 individuals were recruited in the original cohort. Between 1992 and 1993, a total of 687 African-Americans was also recruited. Baseline evaluation of study participants included standardized questionnaires, physical examination, anthropometric measurements, resting electrocardiography, and laboratory examinations. From 1989 through 1999, participants were followed up every 6 months, alternating between telephone calls and clinic visits. The institutional review board at each center approved the study and each participant gave informed consent. Of the total 5,888, we excluded (a) 275 people because of prevalent heart failure; (b) 39 people for missing data on BMI or WC; (c) 472 participants for moderate/severe aortic or mitral regurgitation or stenosis on echocardiograms; and (d) 241 individuals with missing covariate information. Hence, we used 4861 participants for current analyses.
Ascertainment of heart failure
Self-report of a physician diagnosis of heart failure was validated by the CHS Events Committee as previously described(15;27). Briefly, HF validation required a constellation of symptoms (shortness of breath, fatigue, orthopnea, paroxysmal nocturnal dyspnea); chest x-ray findings (pulmonary edema and increasing cardiomegaly); signs (edema, pulmonary rales, gallop rhythm, displaced left ventricular apical impulse); and treatment of HF (diuretics, digitalis, or vasodilators). Incident HF was ascertained upon review of pertinent data on hospitalization or outpatient visits such as medical history, physical examination, report of chest X-ray, and medications. Heart failure was classified as systolic or diastolic heart failure based on left ventricular ejection fraction (LVEF) cut point of 50% in our analysis group. The estimated LVEF was obtained from an echocardiogram, cardiac catheterization, multiple gated cardiac pool imaging, or other modality. Records on EF were obtained by review from CHS investigators or CHS adjudication committee. We had adequate data to estimate LVEF on 730 (53%) HF events in our sample. HF with an LVEF < 50% (n=401) and HF with an LVEF ≥ 50% (n = 329). The current analysis included validated HF through June 30, 2007.
Measurement of anthropometric data
At baseline, trained personnel measured anthropometric data using a standardized protocol. Height was measured in centimeters using a stadiometer and weight was measured using a balance beam scale in pounds while subjects were wearing examination gowns and no shoes. Waist and hip circumferences were measured on standing subjects at the level of the umbilicus and maximal protrusion of the gluteal muscles, respectively. Body mass index was computed as weight (kg) divided by height in meters squared. Waist-hip ratio was calculated as the ratio of waist circumference to hip circumference.
Other variables
Information on race, education, gender, field center, education, income, prevalent chronic diseases (coronary disease, stroke, cancer, valvular or rheumatic heart disease, atrial fibrillation, and diabetes), smoking, alcohol consumption, physical activity, and current medications was obtained during clinic visits. As described previously, standard laboratory methods were used to measure serum albumin, lipids, kidney function, fasting glucose and insulin, and C-reactive protein(28). Usual dietary habits were assessed at baseline in the original cohort using a 99-item picture-sort version of the National Cancer Institute food frequency questionnaire(29). Detailed description of validity and computation of nutrients and energy intake in this cohort has been described previously(29;30). Self-reported
Statistical analysis
Baseline characteristics of study participants were summarized according to sex-specific waist circumference quartiles; continuous variables were presented as mean±standard deviation and categorical variables as percentages. Incidence rates of HF were calculated per 10,000 person-years.
Cox proportional hazards regression was used to estimate the hazard ratio (HR) associated with HF for waist circumference per standard deviation. Individuals were censored for death or end of follow-up. We then adjusted forother baseline risk factors and potential confounders including age, clinic site, race, education (< high school vs. high school or higher), alcohol intake (none, <0.5, 0.5–1, >1 drinks/d for women; none, <1, 1–2, and >2 drinks/d for men), smoking (never, former, and current), kilocalories of physical activity (log-transformed continuous), estimated glomerular filtration rate (using the modification of diet in renal disease (MDRD) study equation(31)), history of physician-diagnosed valvular/rheumatic heart disease, atrial fibrillation by ECG, aspirin use, and estrogen use for women (Model 1). Further adjustment was made for BMI. Additionally, we adjusted for possible intermediate factors at baseline including diabetes, systolic blood pressure, hypertension medication, coronary heart disease, C-reactive protein, triglycerides, high-density and low-density lipoprotein cholesterol (Model 2). We also present results separately for each sex although the interaction of waist circumference and gender was not statistically significant.
In a secondary model, we examined HF with depressed vs. normal LVEF (for 730 HF cases with adequate echocardiographic/imaging data to assess left ventricular ejection fraction), and implemented methods for competing risks(32) while including those with unclassified HF as censored. Using this method, we stratified on HF type (normal vs low LVEF) and estimated the associations for each outcome in the same model with a proportional hazards assumption. Further sensitivity analyses were restricted to subjects without valvular disease, no significant unintentional weight loss, never smoking, and self-reported heath status of good to excellent health. For each analysis, we checked the proportional hazards assumption with Schoenfeld residuals using log(person-time) and plots of the residuals over time; there was no meaningful violation of this assumption.
Results
Of the 4861 participants included in current analyses, 42.5% were men and 15.3% were African-Americans. The mean age was 73.0±5.6 years (range: 65–95) for men and 72.3 ±5.4 years (range: 65–100) for women. Table 1 shows the baseline characteristics of participants according to sex-specific quartiles of waist circumference. During an average follow up of 11.3 years, 1381 incident cases of HF occurred. Pearson’s correlation coefficients between WC and BMI were 0.86 in men and 0.83 in women.
Table 1.
Baseline characteristics according to sex-specific quartiles waist circumference in the Cardiovascular Health Studya
Quartiles of waist circumference (cm) |
|||||
---|---|---|---|---|---|
Q1 (low) | Q2 | Q3 | Q4 (high) | ||
Men | ≤ 91 | 91.1–97.2 | 97.3–104 | >104 | |
Characteristics | Women | ≤ 82 | 82.1–91.6 | 91.7–101.1 | >101.1 |
Age at Baseline | 73.2 ± 5.72 | 72.4 ± 5.6 | 72.6 ± 5.38 | 72.1 ± 5.19 | |
Height (cm) | 164 ± 9.0 | 165 ± 9.46 | 165 ± 9.8 | 166 ± 9.75 | |
Body mass index (kg/m2) | 22.5 ± 2.49 | 25.2 ± 2.46 | 27.3 ± 2.71 | 32.1 ± 4.43 | |
Systolic blood pressure (mm Hg) | 134 ± 21.9 | 135 ± 21.8 | 136 ± 20.8 | 139 ± 21.1 | |
C-reactive protein (mg/dl) | 3.6 ± 7.23 | 4.21 ± 7.53 | 4.61 ± 7.19 | 6.26 ± 8.93 | |
Cystatin C (mg/L) | 1.0 ± .294 | 1.03 ± .368 | 1.04 ± .248 | 1.09 ± .324 | |
eGFR (ml/min/1.73 m2) | 79.2 ± 21.8 | 79.3 ± 23.2 | 78.6 ± 23 | 79 ± 24.3 | |
HDL-cholesterol (mg/dl) | 59.8 ± 17.1 | 54.7 ± 15.6 | 52.9 ± 15.2 | 50 ± 13.1 | |
LDL-cholesterol (mg/dl) | 126 ± 34.4 | 132 ± 35.3 | 132 ± 36.2 | 132 ± 35.4 | |
Triglycerides (mg/dl) | 120 ± 73.4 | 135 ± 71.9 | 147 ± 81 | 158 ± 79 | |
Physical activity (kcal) | 1974 ± 2174 | 1834 ± 2087 | 1753 ± 2019 | 1377 ± 1782 | |
Black race | 10.8% | 12.8% | 15.7% | 21.9% | |
Low-income | 42.8% | 44.4% | 40.4% | 31.4% | |
Education < High School | 26.7% | 24.4% | 27.8% | 34.3% | |
any hypertension medication | 35.4% | 41.6% | 45.9% | 55.6% | |
Aspirin use | 31.5% | 32.7% | 35.4% | 33.7% | |
Valvular/rheumatic disease | 6.2% | 5.1% | 5.0% | 3.3% | |
Prevalent diabetes | 7.5% | 12.3% | 15.7% | 25.6% | |
Prevalent coronary heart disease | 14.4% | 17.4% | 16.3% | 18.0% | |
Prevalent atrial fibrillation | 1.8% | 1.6% | 2.5% | 1.4% | |
Prevalent cancer | 15.5% | 14.3% | 12.3% | 15.1% | |
Prevalent COPD | 13.7% | 11.9% | 12.3% | 14.1% | |
Current smoking | 14.1% | 13.1% | 11.8% | 9.3% | |
Alcohol intake | 51.3% | 56.5% | 51.0% | 44.2% | |
Estrogen use | 15.4% | 13.5% | 11.3% | 9.0% |
Data are means (± standard deviations) or proportions. COPD= chronic obstructive pulmonary disease. There were missing data on following variables: systolic blood pressure (n=5), C-reactive protein (n=25), cystatin C (n=556), triglycerides (n=9), LDL (n=76), HDL (n=16) income (n=298), and prevalent diabetes (n=29).
Each standard deviation (13.2 cm) increase in waist circumference was associated with a 14% (CI: 3%–26%) increased risk of HF upon adjustment for BMI, age, clinic site, gender, race, education, alcohol intake, smoking, physical activity (log-transformed kcals), estimated glomerular filtration rate, valvular/rheumatic heart disease, atrial fibrillation, estrogen use (women), and aspirin use (Table 2). Additional adjustment for hip circumference did not change the results. There was no evidence for a statistically significant interaction between sex and WC on the risk of HF (Table 2). As expected, adjustment for potential intermediate factors (systolic blood pressure, hypertension medications, prevalent diabetes and coronary disease, C-reactive protein (log-transformed), triglycerides, HDL-, and LDL-cholesterol) led to attenuation of the relative risk [HR (95% CI): 1.06 (0.96–1.17)].
Table 2.
Hazard ratios (95% CI) for heart failure per standard deviation increment of waist circumference and body mass indexa
HR (95% CI) per SD of waist circumference (13.2 cm) | HR (95% CI) per SD of body mass index (4.7 kg/m2 | |||||
---|---|---|---|---|---|---|
Crude | Model 1b | Model 1 +BMI | Crude | Model 1b | Model 1 +WC | |
Total population | 1.28 (1.21–1.35) | 1.23 (1.16–1.30) | 1.14 (1.03–1.26) | 1.18 (1.12–1.24) | 1.22 (1.15–1.29) | 1.09 (0.99–1.21) |
Men | 1.31 (1.19–1.44) | 1.31 (1.19–1.45) | 1.23 (1.02–1.50) | 1.23 (1.12–1.35) | 1.28 (1.16–1.42) | 1.07 (0.88–1.30) |
Women | 1.22 (1.14–1.30) | 1.20 (1.11–1.29) | 1.10 (0.98–1.24) | 1.18 (1.10–1.25) | 1.19 (1.11–1.28) | 1.11 (0.98–1.25) |
P value for sex-by-WC or sex-by- BMI interaction |
0.080 | 0.081 | 0.116 | 0.155 |
WC is waist circumference; BMI is body mass index; SD denotes standard deviation; and HR is hazard ratio.
Model 1 adjusted for age, gender, clinic site, ethnicity, education (< high school vs. high school or higher), alcohol intake (none, <0.5, 0.5–1, >1 drinks/d for women; none, <1, 1–2, and >2 drinks/d for men), smoking (never, former, and current), kilocalories of physical activity (log-transformed continuous), estimated glomerular filtration rate, valvular disease, atrial fibrillation by ECG, aspirin use, and estrogen use (for women)
In a sensitivity analysis, exclusion of participants with self-reported poor health status, current or former smokers, and self-reported weight loss yielded a hazard ratio of 1.15 (95% CI: 0.95–1.38). Furthermore, exclusion of people with prevalent valvular/rheumatic disease did not alter the association between WC and HF (data not shown). When using 730 HF cases with echocardiographic data on LVEF, each standard deviation increase of WC was positively associated with HF with lower LVEF (<50%) [HR (95% CI): 1.28 (1.05–1.56)] but not with HF with preserved LVEF (Table 3) in the fully adjusted model. Hip circumference was not independently associated with an increased risk of incident HF after adjustment for BMI and other covariates [HR (95% CI): 1.01 (0.91–1.12) per standard deviation (10.0 cm) of hip circumference). Waist-hip ratio was positively associated with incident HF after adjustment for BMI and other covariates [HR: 1.89 (95% CI: 1.03–3.46)]. Lastly, each standard deviation (4.7 kg/m2) of BMI was associated with a 22% increased risk of incident HF (Table 2); however, control for waist circumference attenuated this relation and rendered it non-statistically significant [HR: 1.09 (95% CI: 0.99–1.21), Table 2].
Table 3.
Hazard ratios (95% CI) for heart failure with and without low left ventricular ejection fraction (LVEF) per standard deviation increment of waist circumference a
HR (95% CI) for heart failure with low LVEF (<50%) |
HR (95% CI) for heart failure with normal LVEF (≥ 50%) |
||||
---|---|---|---|---|---|
Crude | Model 1b | Model 1+BMI | Crude model | Model 1b | Model 1+BMI |
1.29 (1.17–1.41) | 1.19 (1.07–1.33) | 1.28 (1.05–1.56) | 1.26 (1.13–1.40) | 1.27 (1.13–1.42) | 1.11 (0.91–1.36) |
Only 730 heart failure cases had data on echocardiography to characterize LVEF and non-events were used in this Table.
Model 1 adjusted for age, clinic site, gender, ethnicity, education (< high school vs. high school or higher), alcohol intake (none, <0.5, 0.5–1, >1 drinks/d for women; none, <1, 1–2, and >2 drinks/d for men), smoking(never, former, and current), kilocalories of physical activity (log-transformed continuous), estimated glomerular filtration rate, valvular disease, atrial fibrillation by ECG, aspirin use, and estrogen use (for women).
Discussion
In this prospective study of older adults, higher waist circumference was associated with an increased risk of HF after adjustment for BMI and other confounders. In addition, BMI was positively associated with incident HF in a multivariable model; however, after adjustment for waist circumference, the association between BMI and incident HF was attenuated and became non-statistically significant. While waist-hip ratio was positively associated with incident HF, hip circumference was not associated with the risk of HF.
Although several studies have examined the association between adiposity and incident HF, few have assessed whether measures of central adiposity predict HF independent of BMI. Our observation that waist circumference but not BMI, was independently associated with incident HF is consistent with findings of the Health, Aging and Body Composition study,(24) in which each standard deviation increase of waist circumference (but not BMI) was associated with a 27% (95% CI: 4% to 54%) increased risk of HF in a model adjusted for BMI. In a study(33) of 1187 men aged 70+ y, both BMI and waist circumference were positively associated with HF hospitalization; however, investigators in that study did not adjust for BMI when assessing the effects of waist circumference on HF and vice versa. In the HOPE study,(34) BMI, waist-hip ratio, and waist circumference were individually associated with an increased risk of HF in people aged 66 y on average; however, upon additional control for potential mediators including hypertension, diabetes, total and HDL-cholesterol, only waist circumference remained positively associated with incident HF. When stratified by sex, waist circumference and waist-hip ratio but not BMI were positively associated with incident HF in women but not men.(34) Since the HOPE study(34) did not provide sex-specific data that were unadjusted for intermediate factors, it is difficult to contrast the sex-by-central adiposity interaction observed by Dagenais et al.(34) with our results indicating no meaningful sex-by-adiposity interaction on HF risk. Our findings of a stronger association for a central than a general measure of adiposity, are consistent with observations in generally younger populations(25). In a prospective study of women aged 48–83 y, waist circumference was associated with higher risk of HF at all levels of BMI;(25) yet BMI was not associated with increased risk of HF across strata of waist circumference in women.(25) Among men aged 45–79y, waist circumference was positively associated with HF risk in all BMI levels whereas BMI conferred only a modest increased risk of HF across categories of waist circumference.(25) The absence of an association for BMI independent of WC in our cohort may relate to the reported weakening of its association with older age.
Several biological mechanisms might help explain the observed stronger relation between measures of central adiposity (i.e waist circumference) and incident HF. Adipose tissue expresses several hormones including resistin or adiponectin that have been associated with impaired glucose metabolism and/or HF risk. Impaired glucose metabolism has been related to left ventricular systolic and diastolic dysfunction.(35) Hyperinsulinemia can lead to sodium retention(36) and activation of the sympathetic nervous system,(37) factors that can foster HF development. In addition, central obesity is associated with hypertension, dyslipidemia, coronary heart disease, inflammatory state, etc, and could influence the risk of HF via those mediators. The attenuation of the hazard ratio upon additional adjustment for blood pressure, hypertension, prevalent diabetes and coronary disease, C-reactive protein, triglycerides, HDL, and LDL lends support to this hypothesis. Visceral fat (including epicardial fat) expresses several hormones including fatty acid binding proteins with known negative inotropic effects in vitro(38), suggesting that central adiposity may be more important in the development of HF with poor LVEF. Such hypothesis is consistent with our data: each standard deviation of waist circumference was associated with a 28% increased risk of HF with LVEF but only with an 11% (non-statistically significant increased risk of HF with normal LVEF, Table 3). Of note, our secondary analysis on HF with and without normal LVEF was underpowered as we only had adequate information on LVEF on slightly more than half of the HF cases.
Our study has some limitations. As an observational study, we cannot exclude unmeasured confounding as partial explanation to our findings. The inability to classify half of HF cases according to LVEF reduced our statistical power to examine the association between adiposity and HF with preserved or depressed left ventricular systolic function. We were unable to examine the effects of waist circumference across standard categories of BMI due to sparse number of cases in some cells. The generalizability of our findings is limited as our participants were mostly Caucasians. However, our paper has numerous strengths including the large number of male and female subjects with corresponding incident events; the availability of data on numerous covariates including diet, comorbidity, metabolic and lifestyle factors; the use of standardized protocol to collect data; the validation of HF cases by an Endpoint Committee; and the 10+ years of follow up.
In summary, our data showed that a higher waist circumference but not BMI is associated with an increased risk of HF after mutual adjustment, whereas the same was not true of BMI after adjustment for WC. If confirmed in other populations, this suggests that a measure of central adiposity might be a stronger risk factor of HF than overall adiposity.
Acknowledgment
We are indebted to the participants and staff of the Cardiovascular Health Study. A full list of participating CHS investigators and institutions can be found at http://www.chs-nhlbi.org.
Funding: This research was supported by contracts N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222; U01 HL080295; and R01 HL094555 from the National Heart, Lung, and Blood Institute, with additional contribution from the National Institute of Neurological Disorders and Stroke.
Footnotes
Author contribution:
Study design (LD); Statistical analysis (TB); drafting the manuscript (LD); Heart failure adjudication (JSG); critical review of the manuscript (LD, TB, JHI, SJZ, JAD, KJM, DSS, JRK); Obtaining funding (LD,JHX, SZ, KJM, DSS, JRK); Data collection (DS)
Disclosures: None.
References
- 1.Massie BM, Shah NB. Evolving trends in the epidemiologic factors of heart failure: rationale for preventive strategies and comprehensive disease management. Am.Heart J. 1997;133:703–712. doi: 10.1016/s0002-8703(97)70173-x. [DOI] [PubMed] [Google Scholar]
- 2.Lloyd-Jones DM, Larson MG, Leip EP, et al. Lifetime risk for developing congestive heart failure: the Framingham Heart Study. Circulation. 2002;106:3068–3072. doi: 10.1161/01.cir.0000039105.49749.6f. [DOI] [PubMed] [Google Scholar]
- 3.Djousse L, Driver JA, Gaziano JM. Relation between modifiable lifestyle factors and lifetime risk of heart failure. JAMA. 2009;302:394–400. doi: 10.1001/jama.2009.1062. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Gillum RF. Epidemiology of heart failure in the United States. Am.Heart J. 1993;126:1042–1047. doi: 10.1016/0002-8703(93)90738-u. [DOI] [PubMed] [Google Scholar]
- 5.Senni M, Tribouilloy CM, Rodeheffer RJ, et al. Congestive heart failure in the community: a study of all incident cases in Olmsted County, Minnesota, in 1991. Circulation. 1998;98:2282–2289. doi: 10.1161/01.cir.98.21.2282. [DOI] [PubMed] [Google Scholar]
- 6.Goldberg RJ, Spencer FA, Farmer C, Meyer TE, Pezzella S. Incidence and hospital death rates associated with heart failure: a community-wide perspective. Am.J.Med. 2005;118:728–734. doi: 10.1016/j.amjmed.2005.04.013. [DOI] [PubMed] [Google Scholar]
- 7.Goldberg RJ, Glatfelter K, Burbank-Schmidt E, Farmer C, Spencer FA, Meyer T. Trends in mortality attributed to heart failure in Worcester, Massachusetts, 1992 to 2001. Am.J.Cardiol. 2005;95:1324–1328. doi: 10.1016/j.amjcard.2005.01.076. [DOI] [PubMed] [Google Scholar]
- 8.Shahar E, Lee S, Kim J, Duval S, Barber C, Luepker RV. Hospitalized heart failure: rates and long-term mortality. J.Card Fail. 2004;10:374–379. doi: 10.1016/j.cardfail.2004.02.003. [DOI] [PubMed] [Google Scholar]
- 9.Packer M, Coats AJ, Fowler MB, et al. Effect of carvedilol on survival in severe chronic heart failure. N.Engl.J.Med. 2001;344:1651–1658. doi: 10.1056/NEJM200105313442201. [DOI] [PubMed] [Google Scholar]
- 10.Haldeman GA, Croft JB, Giles WH, Rashidee A. Hospitalization of patients with heart failure: National Hospital Discharge Survey, 1985 to 1995. Am.Heart J. 1999;137:352–360. doi: 10.1053/hj.1999.v137.95495. [DOI] [PubMed] [Google Scholar]
- 11.Koelling TM, Chen RS, Lubwama RN, L'Italien GJ, Eagle KA. The expanding national burden of heart failure in the United States: the influence of heart failure in women. Am.Heart J. 2004;147:74–78. doi: 10.1016/j.ahj.2003.07.021. [DOI] [PubMed] [Google Scholar]
- 12.Ho KKL, Pinsky JL, Levy D. The epidemiology of heart failure: the Framingham Study. J Am Coll Cardiol. 1993;22(4) Suppl A:6a–13a. doi: 10.1016/0735-1097(93)90455-a. [DOI] [PubMed] [Google Scholar]
- 13.Ho KK, Anderson KM, Kannel WB, Grossman W, Levy D. Survival after the onset of congestive heart failure in Framingham Heart Study subjects. Circulation. 1993;88:107–115. doi: 10.1161/01.cir.88.1.107. [DOI] [PubMed] [Google Scholar]
- 14.Gheorghiade M, Bonow RO. Chronic heart failure in the United States: a manifestation of coronary artery disease. Circulation. 1998;97:282–289. doi: 10.1161/01.cir.97.3.282. [DOI] [PubMed] [Google Scholar]
- 15.Gottdiener JS, Arnold AM, Aurigemma GP, et al. Predictors of congestive heart failure in the elderly: the Cardiovascular Health Study. J.Am.Coll.Cardiol. 2000;35:1628–1637. doi: 10.1016/s0735-1097(00)00582-9. [DOI] [PubMed] [Google Scholar]
- 16.Hunt SA, Baker DW, Chin MH, et al. ACC/AHA guidelines for the evaluation and management of chronic heart failure in the adult: executive summary. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to revise the 1995 Guidelines for the Evaluation and Management of Heart Failure) J.Am.Coll.Cardiol. 2001;38:2101–2113. doi: 10.1016/s0735-1097(01)01683-7. [DOI] [PubMed] [Google Scholar]
- 17.Wang Y, Beydoun MA. The obesity epidemic in the United States—gender, age socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiol.Rev. 2007;29:6–28. doi: 10.1093/epirev/mxm007. [DOI] [PubMed] [Google Scholar]
- 18.Kenchaiah S, Sesso HD, Gaziano JM. Body mass index and vigorous physical activity and the risk of heart failure among men. Circulation. 2009;119:44–52. doi: 10.1161/CIRCULATIONAHA.108.807289. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Kenchaiah S, Evans JC, Levy D, et al. Obesity and the risk of heart failure. N.Engl.J Med. 2002;347:305–313. doi: 10.1056/NEJMoa020245. [DOI] [PubMed] [Google Scholar]
- 20.Loehr LR, Rosamond WD, Poole C, et al. Association of multiple anthropometrics of overweight and obesity with incident heart failure: the Atherosclerosis Risk in Communities study. Circ.Heart Fail. 2009;2:18–24. doi: 10.1161/CIRCHEARTFAILURE.108.813782. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Snijder MB, van Dam RM, Visser M, Seidell JC. What aspects of body fat are particularly hazardous and how do we measure them? Int.J Epidemiol. 2006;35:83–92. doi: 10.1093/ije/dyi253. [DOI] [PubMed] [Google Scholar]
- 22.Carmelli D, McElroy MR, Rosenman RH. Longitudinal changes in fat distribution in the Western Collaborative Group Study: a 23-year follow-up. Int.J Obes. 1991;15:67–74. [PubMed] [Google Scholar]
- 23.Spies C, Farzaneh-Far R, Na B, Kanaya A, Schiller NB, Whooley MA. Relation of obesity to heart failure hospitalization and cardiovascular events in persons with stable coronary heart disease (from the Heart and Soul Study) Am.J Cardiol. 2009;104:883–889. doi: 10.1016/j.amjcard.2009.05.027. [DOI] [PubMed] [Google Scholar]
- 24.Nicklas BJ, Cesari M, Penninx BW, et al. Abdominal obesity is an independent risk factor for chronic heart failure in older people. J.Am.Geriatr.Soc. 2006;54:413–420. doi: 10.1111/j.1532-5415.2005.00624.x. [DOI] [PubMed] [Google Scholar]
- 25.Levitan EB, Yang AZ, Wolk A, Mittleman MA. Adiposity and incidence of heart failure hospitalization and mortality: a population-based prospective study. Circ.Heart Fail. 2009;2:202–208. doi: 10.1161/CIRCHEARTFAILURE.108.794099. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Fried LP, Borhani NO, Enright P, et al. The Cardiovascular Health Study: design and rationale. Ann.Epidemiol. 1991;1:263–276. doi: 10.1016/1047-2797(91)90005-w. [DOI] [PubMed] [Google Scholar]
- 27.Ives DG, Fitzpatrick AL, Bild DE, et al. Surveillance and ascertainment of cardiovascular events. The Cardiovascular Health Study. Ann.Epidemiol. 1995;5:278–285. doi: 10.1016/1047-2797(94)00093-9. [DOI] [PubMed] [Google Scholar]
- 28.Cushman M, Cornell ES, Howard PR, Bovill EG, Tracy RP. Laboratory methods and quality assurance in the Cardiovascular Health Study. Clin Chem. 1995;41:264–270. [PubMed] [Google Scholar]
- 29.Kumanyika SK, Tell GS, Shemanski L, Martel J, Chinchilli VM. Dietary assessment using a picture-sort approach. Am J Clin Nutr. 1997;65:1123S–1129S. doi: 10.1093/ajcn/65.4.1123S. [DOI] [PubMed] [Google Scholar]
- 30.Mozaffarian D, Kumanyika SK, Lemaitre RN, Olson JL, Burke GL, Siscovick DS. Cereal, fruit, and vegetable fiber intake and the risk of cardiovascular disease in elderly individuals. JAMA. 2003;289:1659–1666. doi: 10.1001/jama.289.13.1659. [DOI] [PubMed] [Google Scholar]
- 31.Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann.Intern.Med. 1999;130:461–470. doi: 10.7326/0003-4819-130-6-199903160-00002. [DOI] [PubMed] [Google Scholar]
- 32.Lunn M, McNeil D. Applying Cox regression to competing risks. Biometrics. 1995;51:524–532. [PubMed] [Google Scholar]
- 33.Ingelsson E, Sundstrom J, Arnlov J, Zethelius B, Lind L. Insulin resistance and risk of congestive heart failure. JAMA. 2005;294:334–341. doi: 10.1001/jama.294.3.334. [DOI] [PubMed] [Google Scholar]
- 34.Dagenais GR, Yi Q, Mann JF, Bosch J, Pogue J, Yusuf S. Prognostic impact of body weight and abdominal obesity in women and men with cardiovascular disease. Am.Heart J. 2005;149:54–60. doi: 10.1016/j.ahj.2004.07.009. [DOI] [PubMed] [Google Scholar]
- 35.Arnlov J, Lind L, Zethelius B, et al. Several factors associated with the insulin resistance syndrome are predictors of left ventricular systolic dysfunction in a male population after 20 years of follow-up. Am.Heart J. 2001;142:720–724. doi: 10.1067/mhj.2001.116957. [DOI] [PubMed] [Google Scholar]
- 36.Defronzo RA, Cooke CR, Andres R, Faloona GR, Davis PJ. The effect of insulin on renal handling of sodium, potassium, calcium, and phosphate in man. J Clin Invest. 1975;55:845–855. doi: 10.1172/JCI107996. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Anderson EA, Hoffman RP, Balon TW, Sinkey CA, Mark AL. Hyperinsulinemia produces both sympathetic neural activation and vasodilation in normal humans. J Clin Invest. 1991;87:2246–2252. doi: 10.1172/JCI115260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Lamounier-Zepter V, Look C, Alvarez J, et al. Adipocyte fatty acid-binding protein suppresses cardiomyocyte contraction: a new link between obesity and heart disease. Circ.Res. 2009;105:326–334. doi: 10.1161/CIRCRESAHA.109.200501. [DOI] [PubMed] [Google Scholar]