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Keywords:

  • Obesity;
  • heart failure

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. References

Purpose

Heart failure (HF) patients often have comorbid conditions that confound management and adversely affect prognosis. The purpose of this study was to determine whether the obesity paradox is also present in hospitalized HF patients in an integrated healthcare system.

Data sources

A cohort of 2707 patients with a primary diagnosis of HF was identified within an integrated, 20-hospital healthcare system. Patients were identified by ICD-9 codes or a left ventricular ejection fraction ≤40% dating back to 1995. Body mass index (BMI) was calculated using the first measured height and weight when hospitalized with HF. Survival rates were calculated using Kaplan Meier estimation. Hazard ratios for 3-year mortality with 95% confidence intervals were assessed using Cox regression, controlling for age, gender, and severity of illness at time of diagnosis.

Conclusions

Three-year survival rates paradoxically improved for patients with increasing BMI. Survival rates for the larger three BMI quartiles were significantly better than for the lowest quartile after adjusting for severity of illness, age, and gender.

Implications for practice

While obesity increases the risk of developing HF approximately twofold, reports involving stable outpatients suggest that obesity is associated with improved survival after the development of HF. This finding is paradoxical because obesity increases the risk and worsens the prognosis of other cardiovascular diseases.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. References

Heart failure (HF) is a major public health problem reaching epidemic proportions. It affects 4.9 million Americans, with over 550,000 new HF cases diagnosed each year in the United States (American Heart Association [AHA], 2004). Another even more startling statistic relates to the increasing prevalence of obesity (as defined by a body mass index [BMI] greater than 30 kg/m2) in the United States. Obesity affects over one third of the population (Coviello & Nystrom, 2003; Kopelman, 2000; Kuczmarski, Flegal, Campbell, & Johnson, 1994; Mokdad, Serdula, & Dietz, 1999; National Institutes of Health, 1998; World Health Organization, 1998). Over 127 million individuals are overweight, 60 million are obese, and 9 million are severely obese (http://www.cdc.gov/nccdphp/dnpa/obesity).

Both HF and obesity independently contribute to comorbid conditions, hospitalizations, escalating medical costs, and increased risk of death. HF mortality rates have increased by 148% from 1979 to 2000 (AHA, 2004; Clark, 2003; Coviello & Nystrom, 2003). Over 260,000 people die annually of HF in the United States; over 75% of patients diagnosed with HF die within 8 years (AHA). Obesity is independently associated with HF and contributes to the development of additional HF risk factors, including hypertension, left ventricular hypertrophy, and diastolic filling abnormalities (Greenberg & Hermann, 2002; He et al., 2001; Kenchaiah et al., 2002; Kenchaiah, Gaziano, & Vasan, 2004; Koch & Sharma, 1999; Schunkert, 2002; Vasan, 2003; Wilhelmsen, Rosengren, Eriksson, & Lappas, 2001). Obesity is also an independent risk factor for coronary heart disease (Eckel, 1997; Krauss, Winston, Fletcher, & Grundy, 1998).

While the obese have a twofold risk of developing overt HF and even subclinical left ventricular dysfunction (Peterson et al., 2004; Wong et al., 2004), reports involving stable outpatients suggest that obesity is associated with improved survival after developing HF (Alla, Briancon, & Juilliere, 2000; Davos et al., 2003; Horwich & Fonarow, 2002; Kalantar-Zadeh, Block, Horwich, & Fonarow, 2004; Lavie, Osman, Milani, & Mehra, 2003; Selter et al., 2003). This finding is paradoxical because obesity increases the risk and worsens the prognosis of other cardiovascular diseases. In fact, an increasing body of evidence has shown that those who are obese are at risk of over 30 health conditions including coronary artery disease, diabetes, and hypertension, and subsequently have higher mortality rates (Allison, Fontaine, & Manson, 1999; Field, Coakley, & Must, 2001).

While the literature is replete with information that obesity increases the risk of developing HF, the “obesity paradox” has been observed when the risk of mortality is less in those who are the most obese (Curtis et al., 2005). The purpose of this study was to determine whether the obesity paradox is also present in hospitalized HF patients in an integrated healthcare system.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. References

Within our 20-hospital integrated healthcare delivery system, extensive data-tracking capabilities are available through an electronic data warehousing system. A retrospective review was performed. A cohort of 2707 patients with the primary diagnosis of HF was initially identified dating back to 1995. Patients were identified by ICD-9 coding or by the identification of a left ventricular ejection fraction ≤40% across the system within the data warehousing system. BMI was calculated using the first measured height and weight when hospitalized with HF. These initial measurements were chosen because all patients admitted to our hospital facilities receive an admission height and weight assessment. The instrument used to calculate BMI was the formula using weight in pounds and height in inches approved by the Division of Nutrition and Physical Activity, National Center for Chronic Disease Prevention and Health Promotion (http://www.cdc.gov/nccdphp/dnpa/bmi/index.htm). BMI quartiles were defined as BMI <24.3, BMI 24.4–28.5, BMI 28.6–34.1, and BMI ≥34.2.

Our institution's Information Security Committee provided ethical oversight and approval to this research project. This study was completely epidemiologic using data currently available within our system's clinical information systems and did not involve treatment interventions or patient contact. No individually identifiable patient information was used in the analysis. All results were presented as summary statistics so that there was no risk to individual patients regarding disclosure of their personal health information.

Survival rates were calculated using Kaplan Meier estimation. Hazard ratios for 3-year mortality with 95% confidence intervals were assessed using Cox regression, controlling for age, gender, and severity of illness at the time of initial diagnosis.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. References

Survival following the diagnosis of HF among patients with different BMIs is shown in 1Figure 1. Three-year survival rates paradoxically improved with increasing BMI. Because the paradox shown in Figure 1 could be influenced by variables other than BMI, analyses were performed after adjusting for age, gender, and severity of HF at the time of presentation as shown in 1Table 1. After adjustment, survival rates for the larger three BMI quartiles (II, III, IV) were significantly better than for the lowest quartile (I).

image

Figure 1. Survival, by BMI category.

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Table 1. BMI Quartiles
 Hazard Ratioa(95% CI) p Value
BMI QuartileIIIIIIV
  • a

    Adjusted for severity of illness, age, and gender.

I0.77 (0.63, 0.94) p < 0.0010.70 (0.56, 0.86) p < 0.0010.77 (0.59, 0.99) p= 0.027
II 0.92 (0.74, 1.15) p= 0.2231.03 (0.79, 1.34) p= 0.415
III 1.05 (0.81, 1.36) p= 0.364

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. References

In a multihospital, integrated healthcare delivery system, an increased BMI was associated with improved survival in hospitalized patients with HF. This retrospective review demonstrates that survival is improved in these obese HF patients, a paradoxical finding to the effects obesity has on the non-HF population. Given that nearly 25% of the study patients had a BMI over 34, these data also illustrate that the obese population within this cohort of patients with HF is growing.

Why the obesity paradox may occur in patients with HF is unclear. HF patients may be selectively different. Because there appears to be a catabolic state within HF (Berry & Clark, 2000; Coats et al., 2001), it may be that heavier patients maintain a greater metabolic reserve and therefore can tolerate the catabolic effects of HF to a greater extent (Anker et al., 1999; Davos et al., 2003; Kenchaiah et al., 2004). Because obese individuals are at risk for hypertension, they may be better able to tolerate optimal doses of antihypertensives used to treat HF. The National Health and Nutrition Examination Survey (Masoudi, Havranek, & Krumholz, 2002) identified over 50 million patients with cardiac or vascular diseases; however, only 10% of these patients suffer from HF. It could be postulated that those patients with HF have other protective factors, which have allowed them to reach even this stage of the disease (Kalantar-Zadeh et al., 2004). A patient's left ventricular myocardial characteristics associated with obesity have shown a greater tendency toward diastolic dysfunction, rather than systolic dysfunction (Wong et al., 2004).

The stages of HF (2Table 2) have been recognized since 2002 (Hunt et al., 2001) as a means to understand the continuum of HF, from risk factor identification through treatment of end-stage disease. Stage A HF includes those at risk of developing HF, with treatment aimed at reducing risks such as obesity, hypertension, diabetes, and hyperlipidemia. One clearly sees the benefits of aggressive risk factor modification for those patients in stage A and subsequent impact on preventing progression to left ventricular dysfunction and overt symptoms. Strategies to control obesity can have profound effects on decreasing hypertension, hyperlipidemia, and diabetes, and are likely to have the greatest chance of reducing HF risks.

Table 2. American College of Cardiology/AHA HF Stages
AAt high risk for HF but without structural heart disease or symptoms of HF (e.g., patients with hypertension or coronary artery disease)
BStructural heart disease but without symptoms of HF
CStructural heart disease with prior or current symptoms of HF
DRefractory HF requiring specialized interventions

This paradoxical response is not unique to HF patients but is also seen in those diagnosed with malignancies and patients with end-stage renal disease (Chang, Barrett-Connor, & Edelstein, 1995; Kalantar-Zadeh et al., 2004; Landi, Onder, & Gambassi, 2000; Stevens, Cai, & Pamuk, 1998). Kalantar-Zadeh and colleagues suggest that a malnutrition–inflammation complex syndrome could contribute to the paradox seen in these various populations. According to this theory, malnutrition increases with the degree of inflammatory responses, which adversely affects mortality. Because HF does have physiological effects linked with the inflammatory process, this theory could apply. Their recommendation is that providers should focus on optimal management of undernutrition and inflammation.

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. References

In conclusion, this study has shown evidence to support others’ findings that an elevated BMI portends a better prognosis in HF patients, specifically in those with systolic dysfunction and a primary diagnosis of HF. This in no manner explains why HF in the obese patients represents a less morbid disease than in nonobese patients. One might therefore surmise that HF patients should strive for a “greater than ideal” body weight; however, such a recommendation clearly warrants further investigation. Because obesity poses serious risks to those without HF, it is the burden of researchers in the HF community to make sense of the obesity paradox. Better understanding and comparison of metabolic reserve and the catabolic effects of HF may elucidate explanations and provide future recommendations in the management strategies.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. References
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