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. Author manuscript; available in PMC: 2010 Jul 28.
Published in final edited form as: J Am Coll Cardiol. 2009 Jul 28;54(5):422–428. doi: 10.1016/j.jacc.2009.04.049

Relationship of Hemoglobin A1C and Mortality in Heart Failure Patients with Diabetes

David Aguilar 1,2, Biykem Bozkurt 1,2, Kumudha Ramasubbu 1,2, Anita Deswal 1,2,3
PMCID: PMC2753214  NIHMSID: NIHMS134294  PMID: 19628117

Abstract

Objectives

To examine the relationship between glycosylated hemoglobin (HbA1C) and adverse outcomes in diabetic patients with established heart failure (HF).

Background

Despite the common co-existence of diabetes and HF, previous studies examining the association between HbA1C and outcomes in this population have been limited and have reported discrepant results.

Methods

We assessed the association between increasing quintiles (Q1-Q5) of HbA1C and risk of death or risk of HF hospitalization by conducting a retrospective study in a national cohort of 5815 veterans with HF and diabetes treated in ambulatory clinics at Veterans Affairs medical centers.

Results

At two years of follow-up, death occurred in 25% of patients in Q1 (HbA1C ≤ 6.4), 23% in Q2 (6.4 < HbA1c ≤ 7.1), 17.7% in Q3 (7.1 < HbA1c ≤ 7.8), 22.5% in Q4 (7.8 < HbA1c ≤ 9.0), and 23.2% in Q5 (HbA1c > 9.0). After adjustment for potential confounders, the middle quintile (Q3) had reduced mortality when compared to the lowest quintile (risk-adjusted HR 0.73, 95% CI 0.61 to 0.88, p=0.001). HF hospitalization rates at 2 years increased with increasing quintiles of HbA1C (Q1 13.3%, Q2 13.1%, Q3 15.5%, Q4 16.4%, and Q5 18.2%), but this association was not statistically significant when adjusted for potential confounders.

Conclusions

The association between mortality and HbA1C in diabetic patients with heart failure appears U-shaped, with the lowest risk of death in those patients with modest glucose control (7.1% < HbA1C ≤ 7.8%). Future prospective studies are necessary to define optimal treatment goals in these patients.

Keywords: Diabetes, Heart Failure, Glycosylated Hemoglobin, HbA1C, prognosis

Diabetes mellitus and heart failure (HF) are major health problems. There are nearly 5 million individuals who have HF and over 500,000 new cases are diagnosed each year in the United States (1). It has been well-established that diabetes, a disease that is increasing in prevalence (2), is a significant risk factor for the development of cardiovascular disease(3) and amplifies the risk for the development of HF (4-6). In addition, HF itself is considered an insulin resistant state and is associated with significant risk for the future development of diabetes (7). Given these relationships, it is not surprising that diabetes and heart failure commonly co-exist. The prevalence of diabetes in major HF trials is approximately 20−30% (8), although recent data from an acute HF registry suggest that the prevalence may be as high as 45% (9). Importantly, in cohorts of patients with established HF, diabetes has been associated with increased mortality and morbidity (10-12). Given the scope and growing burden of diabetes and HF, efforts to understand risk factors contributing to this increased hazard are increasingly important.

One potential risk factor associated with adverse outcomes is poor glycemic control. Glycosylated hemoglobin (HbA1C) reflects the ambient blood glucose over the preceding 2 to 3 months, is used as an index of mean glycemia (13), and serves as a treatment target in patients with diabetes (14). In individuals free of HF, elevated HbA1C has been associated with an increased risk of adverse cardiovascular outcomes (15), including increased risk of incident HF (16). Despite these data, studies examining the association between HbA1C and outcomes in diabetic patients with established HF have been limited and have reported discrepant results (17,18). Therefore, we sought to determine the association between HbA1C and total mortality or HF hospitalization in a large, national cohort of ambulatory diabetic patients with established HF.

METHODS

Study Design and Sample

We performed a retrospective study of a national cohort of veterans with HF treated in ambulatory clinics at Veteran Affairs (VA) medical centers using the VA External Peer Review Program (EPRP) data between October 2000 and September 30, 2002. As described in detail previously (19,20), the sampling pool of outpatients for EPRP included ambulatory patients with common chronic diseases such as HF, diabetes, ischemic heart disease, and chronic obstructive pulmonary disease (COPD) identified by specific ICD-9 codes. Abstractors reviewed electronic medical records for validation of sample selection criteria, including documentation of a HF diagnosis in outpatient records (20). The overall outpatient cohort for the EPRP for this time period included 21,794 outpatients with HF. Patient-level data from the EPRP HF cohort was linked with five existing national VA databases to obtain further demographic, laboratory, pharmacy and outcome data.

Individuals from the EPRP HF cohort who had diabetes as identified in the EPRP data (n=8842), were prescribed hypoglycemic medications in the pharmacy database (n=7148), and had a recorded HbA1C level (in the laboratory database) within one year prior or up to 2 weeks after the index outpatient visit were included for this analysis (n=5815). Diabetic therapy was ascertained using pharmacy data and was based on prescriptions filled 90 days prior to or 30 days following the index outpatient visit. Baseline demographics and concomitant cardiac medications were assessed at the index visit. A covariate that reflected diabetes severity included a variable documenting a diabetic complication including neuropathy, nephropathy, retinopathy, or peripheral vascular disease. For laboratory data, the most recent laboratory data within one year prior and up to two weeks after the index clinic visit were used. Glomerular filtration rate (GFR) was calculated using the 4-variable Modification of Diet in Renal Disease equation (21).

Individuals were classified into categories based on quintiles of HbA1C. The levels of HbA1C in each quintile were as follows: quintile 1 (Q1): HbA1C ≤ 6.4%; quintile 2 (Q2) 6.4% < HbA1C ≤ 7.1%; quintile 3 (Q3) 7.1% < HbA1C ≤ 7.8%; quintile 4 (Q4) 7.8% < HbA1C ≤ 9.0%, and quintile 5 (Q5) HbA1C > 9.0%.

The primary outcomes were time to death or time to hospitalization for HF over two years after the index outpatient visit. Complete follow-up over the two year period was available for all participants.

Statistical Analysis

Differences in baseline variables were ascertained using chi-square tests for categorical variables and analysis of variances (ANOVA) for continuous variables. Two-sided p-values < 0.05 were considered statistically significant. Univariate and multivariable Cox proportional hazards models were used to assess the relationship between outcomes and quintiles of HbA1c (using Q1 as the reference group). In the Cox proportional hazards model for HF hospitalization, the patients were censored when they died. From 29 candidate variables collected at baseline (including diabetic therapy), separate multivariable Cox proportional hazards models for death and for hospitalization for HF were constructed using forward stepwise selection with variables with p ≤ 0.1 being entered into the model. Variables common to both models included: left ventricular ejection fraction (LVEF), GFR, hemoglobin, hyponatremia, COPD, atrial fibrillation, race, cancer, and previous HF hospitalization within the last two years. The multivariable model for death also included main effects of age, gender, angiotensin converting enzyme inhibitor or angiotensin II receptor blocker use, beta-blocker use, statin use, peripheral vascular disease, dementia, sulfonylurea use, and biguanide use. The multivariable model for HF hospitalization included diabetes with complications, insulin use, psychiatric disorders, and rheumatologic disease. Missing laboratory values (other than HbA1C) were imputed using the median value of the study cohort for that parameter [missing=245 (4.2%) for serum sodium, 387 (6.7%) for serum creatinine, 752 (12.9%) for total cholesterol, and 1035 (17.8%) for hemoglobin)] and a dummy variable was used to indicate replacement of missing data. Models including only patients without missing laboratory data yielded no significant change in overall results. Statistical analysis was performed using Intercooled Stata 9.2 for Windows (StataCorp LP, Colleged Station, TX).

RESULTS

The study cohort consisted of 5815 diabetic patients with HF. The cohort, consistent with VA medical centers population, was 94% men with a mean age of 69.2 ± 9.2 years. The mean BMI was 31.7 ± 6.7 kg/m2. Thirty five percent of patients had a previous myocardial infarction. A total of 4840 (83%) patients had a documented assessment of left ventricular ejection fraction (LVEF); of these patients, 54.5% had preserved or mildly reduced LVEF (LVEF ≥ 40%) and 45.5% had moderate or severely reduced ejection fraction.

The mean HbA1C level of the cohort was 7.75% ± 1.7%. Patient characteristics for each quintile are shown in Table 1. Individuals in the lower quintiles tended to be older, to have a lower GFR, hemoglobin, and total cholesterol, and were more likely to have preserved LVEF. As the HbA1C quintiles increased, there was increased prevalence of diabetic complications, and individuals were more likely require a greater number of medications for glycemic therapy and were more likely to be receiving insulin therapy. Individuals in the higher quintiles were more likely to receive angiotensin converting enzyme inhibitors or angiotensin II receptor blockers and statins.

Table 1.

Clinical Characteristics

Quintile 1: HbA1C ≤ 6.4 (n=1264) Quintile 2: 6.4 < HbA1C ≤ 7.1 (n=1152) Quintile 3: 7.1 < HbA1C ≤ 7.8 (n=1092) Quintile 4: 7.8 < HbA1C ≤ 9.0 (n=1198) Quintile 5 HbA1C > 9.0 (n=1109) p-value
Age, yrs 70.5 ± 9.3 70.5 ± 9.0 69.6 ± 8.9 68.8 ± 9.1 66.2 ± 9.9 <0.001
Male, % 93.4 94.1 92.8 94.6 92.6 0.25
Race, % <0.001
    White 76.0 75.4 77.7 75.9 71.8
    Black 12.0 9.4 10.7 11.3 16.6
    Other/unknown 12.0 15.3 11.6 12.9 11.6
BMI, kg/m2 31.3 ± 6.9 31.7± 6.6 31.8 ± 6.4 31.7 ±6.6 32.1 ± 7.0 0.05
SBP, mm Hg 131 ±21.4 131 ± 20.5 132 ± 21.8 131 ± 21.5 132 ± 22.2 0.18
LVEF, % <0.001
    Normal or mildly reduced 48.0 48.7 45.5 44.5 39.5
    Moderate or severely reduced 33.3 33.4 37.9 40.1 45.5
    Unknown 18.7 17.9 16.6 15.4 15.1
Diabetes with complications, % 51.7 55.0 57.8 62.2 64.1 <0.001
Peripheral Vascular Disease, % 22.2 23.7 21.6 25.3 27.0 0.02
Atrial Fibrillation, % 29.3 28.3 29.0 27.9 28.9 0.94
Past Myocardial Infarction, % 34.7 33.5 37.1 36.1 33.0 0.21
Prior HF hospitalization within 2 years, % 17.7 14.7 16.8 18.7 22.3 <0.001
COPD, % 29.5 28.0 24.6 26.0 25.5 0.05
Cancer, % 18.4 16.9 16.9 15.8 14.3 0.08
HbA1C, % 5.9 ± 0.5 6.8 ± 0.2 7.5± 0.2 8.4 ± 0.3 10.5 ± 1.7 <0.001
GFR, ml/min/m2 54.8 ± 22.7 55.2 ± 21.5 55.3 ± 21.6 55.5 ± 21.5 58.3 ± 23.3 <0.001
Hemoglobin, mg/dL 13.1 ± 1.9 13.3 ± 1.7 13.4 ± 1.7 13.4 ± 1.6 13.6 ± 1.7 <0.001
Cholesterol, mg/dL 166 ± 39.0 168 ± 36.4 169 ± 37.4 172 ± 40.1 180 ± 42.6 <0.001
Medications
    Insulin, % 33.6 41.2 48.2 57.9 64.4 <0.001
    Sulfonlyurea, % 63.9 60.7 58.7 55.9 51.1 <0.001
    Biguanide 20.4 25.1 26.2 28.1 29.4 <0.001
    TZD, % 7.9 10.7 11.8 13.3 16.4 <0.001
Other diabetic therapy, % 1.0 1.6 1.5 1.3 2.6 0.03
Mean no. of diabetic drugs receiving 1.3 1.4 1.5 1.6 1.6 <0.001
ACE/ARB, % 80.5 83.3 84.0 84.1 86.4 0.004
Beta-blocker, % 60.4 59.5 59.9 61.1 62.3 0.67
    Statin, % 52.6 55.6 56.8 60.5 59.2 0.001
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Data expressed as mean ± SD or percentage. BMI= body mass index; SBP= systolic blood pressure; LVEF= left ventricular ejection fraction; COPD= chronic obstructive pulmonary disease; GFR=glomerular filtration rate; TZD= thiazolidinedione; ACE/ARB= angiotensin converting enzyme inhibitor or angiotensin II receptor blocker.

Mortality

Over 2 years of follow-up, 316 (25.0%) Q1 patients, 265 (23.0%) Q2 patients, 193 (17.7%) Q3 patients, 269 (22.5%) Q4 patients, and 257 (23.2%) Q5 patients died (Figure 1 and Table 2). The unadjusted and adjusted hazard ratios for death are shown in Table 2. Using Q1 as the reference group, the middle quintile (Q3) had significantly reduced mortality [risk-adjusted hazard ratio (HR) 0.73, 95% CI 0.61 to 0.88, p=0.001]

Figure 1. Proportion of Patients who Died at 2-year Follow-up by Quintiles of HbA1C.

Figure 1

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Global chi-square p-value=0.001. Error bars indicate the 95% confidence intervals.

Table 2.

Outcomes

OUTCOME Quintile 1: HbA1C ≤ 6.4 (n=1264) Quintile 2: 6.4 < HbA1C ≤ 7.1 (n=1152) Quintile 3: 7.1 < HbA1C ≤ 7.8 (n=1092) Quintile 4: 7.8 < HbA1C ≤ 9.0 (n=1198) Quintile 5 HbA1C > 9.0 (n=1109)
Death, n (%) 316 (25.0) 265 (23.0) 193 (17.7) 269 (22.5) 257 (23.2)
    Unadjusted HR (95% CI)* 1.00 0.89 (0.76−1.05, p=0.17) 0.67 (0.56−0.80, p<0.001) 0.87 (0.74−1.03, p=0.11) 0.91 (0.77−1.07, p=0.26)
    Adjusted HR (95% CI)*† 1.00 0.96 (0.81−1.13, p=0.63) 0.73 (0.61−0.88, p=0.001) 0.96 (0.81−1.13, p=0.61) 1.06 (0.90−1.26, p=0.48)
HF Hospitalization, n (%) 168 (13.3) 151 (13.1) 169 (15.5) 196 (16.4) 202 (18.2)
    Unadjusted HR (95% CI)* 1.00 0.96 (0.77−1.20, p=0.72) 1.14 (0.92−1.41, p=0.24) 1.22 (0.99−1.49, p=0.06) 1.39 (1.13−1.70, p=0.002)
    Adjusted HR (95% CI)*† 1.00 1.01 (0.81−1.26, p=0.96) 1.14 (0.92−1.41, p=0.25) 1.11 (0.90−1.37, p=0.34) 1.17 (0.94−1.44, p=0.16)
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*

Quintile 1 is the reference group.

†

Text describes multivariable model

Given the crude U-shaped relationship between mortality and quintiles of HbA1C levels, additional analyses were performed with multivariable Cox proportional hazard models using the middle quintile (Q3) as the reference group. Compared to Q3, the other four quintiles had significantly increased risk of death over two years (risk-adjusted HR: Q1 HR 1.37 (95% CI 1.14−1.64, p=0.001), Q2 HR 1.31 (95% CI 1.09−1.58, p=0.004), Q4 HR 1.31 (95% CI 1.09−1.58, p=0.004), Q5 HR 1.45 (95% CI 1.20−1.76, p<0.001).

Additional analyses (data not shown) demonstrated similar relationships between HbA1C quintiles and mortality when patients were stratified according to LVEF (preserved or mildly reduced vs. moderate to severely reduced), renal insufficiency (GFR ≥ 60 vs. GFR <60), advanced age (age ≥ 65 years versus age <65 years), and insulin use.

Heart Failure and All-Cause Hospitalization

The incidence of HF hospitalization over two years increased with increasing levels of HbA1C (Figure 2 and Table 2). In adjusted analyses (using Q1 as the reference group), the hazard of HF hospitalization increased with increasing quintiles, but this hazard was no longer statistically significant after adjustment for potential confounders (Table 2).

Figure 2. Proportion of Patients with HF Hospitalization at 2-year Follow-up by Quintiles.

Figure 2

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Global chi-square p-value=0.002. Error bars indicate the 95% confidence intervals.

Similarly, the incidence of all-cause hospitalization increased across the HbA1C quintiles. In total, 550 (43.5%) Q1 patients, 491 (42.6%) Q2 patients, 492 (45.1%) Q3 patients, 564 (47.1%) Q4 patients, and 572 (51.6 %) Q5 patients were hospitalized for any cause during the 2 years of follow-up. After adjustments for potential confounders, there was a trend towards increased all-cause hospitalization in the highest quintile (Q5) when compared to the lowest quintile (Q1) (risk-adjusted HR 1.12, 95% CI 0.99−1.26, p=0.08). The hazard ratios for the other quintiles were not significantly different than the first quintile (data not shown).

DISCUSSION

In a cohort of ambulatory patients with established HF who were receiving medical treatment for diabetes, the relationship between mortality and HbA1C was U-shaped. Individuals in the middle quintile with modest glycemic control (7.1% < HbA1C ≤ 7.8%) had the lowest mortality when compared to the other quintiles of HbA1C. Although the rates of HF hospitalization increased with increasing levels of HbA1C, this crude relationship was no longer statistically significant after adjustment for potential confounders, suggesting that the associated baseline demographic and treatment differences seen in patients with elevated HbA1c levels may be accounting for the increased rates of HF hospitalization.

Although elevated HbA1C has been associated with increased risk of adverse cardiovascular events in the general population (15), the relationship between HbA1C levels and prognosis in diabetic patients with established HF has been less well studied. A recent analysis of 2412 HF participants (of which only 907 participants had prior diabetes) enrolled in the CHARM program demonstrated that the adjusted risks of total mortality, HF hospitalization, and a composite outcome of cardiovascular death or HF hospitalization increased progressively with increasing levels of HbA1C (18). Of note, this relationship between HbA1C levels and death in CHARM was more pronounced in the non-diabetic patients enrolled in CHARM and did not reach statistical significance for the outcomes of cardiovascular death (p for heterogeneity=0.04) and total mortality (p for heterogeneity=0.008) in the 907 patients with prior diabetes after adjustment for differences in baseline characteristics. Additionally, in a smaller study of 123 diabetic patients with advanced systolic HF referred to a HF management program, a paradoxical relationship between HbA1C levels and mortality was identified (17). In this study of 123 patients with advanced systolic HF, a HbA1C level ≤ 7% was associated with an increased risk of death when compared to a level of >7%, even after adjustments for potential confounders.

Our data add to this existing body of literature and support a more complex relationship between HbA1C and mortality in diabetic patients with established HF. By studying a large sample of ambulatory HF patients with established diabetes, we demonstrate that patients in the lower and higher HbA1C quintiles have a higher mortality than patients with modest glycemic control (7.1% < HbA1C ≤ 7.8%). The increased mortality associated with the higher HbA1C levels is likely multifactorial and may include both direct and indirect effects of hyperglycemia. Potential adverse effects of hyperglycemia include increased endothelial dysfunction (22), increased oxidative stress (23), increased protein kinase C activation (24), and potentially accelerated atherosclerosis. In addition, increased advanced glycation end products (AGEs), as a result of chronic hyperglycemia, may lead to a variety of detrimental processes such as increased myocardial stiffness (25) and activation of the receptor for AGEs which in turn leads to upregulation of cellular signals which lead to cellular dysfunction (26,27). Also, elevated levels of HbA1C may be a marker for a greater degree of insulin resistance with the associated derangements of cardiac metabolism and myocardial energy utilization in the insulin resistant myocardium (28) and increased activation of the sympathetic nervous system (29). Finally, elevated HbA1C levels may also be reflective of poor compliance with medications which in turn may be associated with poor outcomes.

In addition to the increased risk of death in the higher HbA1C quintiles, we also demonstrate that lower quintiles of HbA1C are associated with an increased mortality when compared to those patients with modest glycemic control (7.1% < HbA1C ≤ 7.8%). There are several possible explanations for these findings. First, intensive glucose therapy to achieve normal or near-normal HbA1C levels may be hazardous in diabetic patients with established HF. In the recently completed Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, a strategy of intensive glucose control targeting normal levels of HbA1C (below 6%) to reduce cardiovascular events was compared with standard therapy (targeting a level from 7.0 to 7.9%) in patients with type 2 diabetes and either established cardiovascular disease or additional cardiovascular risk factors (30). In the ACCORD trial, there was an unexpected increase in mortality in patients assigned to the intensive treatment arm with no significant reduction in major cardiovascular events when compared to patients receiving standard care. Our data differ from ACCORD in that patients who achieved the lowest HbA1C levels in ACCORD were also receiving more antihyperglycemic medications than those in the standard therapy group. Nonetheless, our data, in a higher risk population, are consistent with the unexpected hazard associated with a strategy of intensive glucose control that was seen in the ACCORD study. A second possible explanation for our findings includes “reverse epidemiology” (31,32). This concept refers to the observation of several, paradoxical relationships between conventional cardiovascular risk factors and clinical outcomes in patients with established HF. Such risk factors have included obesity and hypercholesterolemia, in which higher levels of body weight or cholesterol are paradoxically associated with increased survival in patients with established HF (31). Although there are several potential causes for this risk factor pattern, more commonly proposed explanations include the association of lower weight and lower cholesterol with increased protein energy malnutrition and an increased inflammatory syndrome associated with cardiac cachexia (31,32). Of note, the mean body mass index (BMI) was slightly lower in the lowest HbA1C quintile in this study, but the association between HbA1C and mortality remained statistically significant after adjusting for small differences in BMI. Another potential explanation for our findings is that low HbA1C is a marker for more advanced or severe HF or a marker for older patients with more comorbid conditions. It is important to note that functional status was not available in this cohort, but patients in the lowest HbA1C quintile were more likely to have preserved LVEF than those patients with increased HbA1C levels. Finally, low HbA1C may not be an adequate marker of glycemic control and/or may be reflective of an unmeasured variable that may be contributing to adverse events.

Limitations

The present study has several limitations. First, the study is an observational study with inherent limitations of this type of study design. Multivariable statistical models were used to adjust for heterogeneity between HbA1C groups, but residual unmeasured confounding may remain. However, availability of data on LVEF, pharmacy data (including diabetic therapy), and laboratory data should provide robust risk-adjustment variables. In addition, these data were collected as part of a national clinical database at VA medical centers, and measurements of HbA1C were performed in a clinical setting and not in a standardized laboratory. Also, the most recent HbA1C value within one year prior and up to two weeks after the index visit were used as the baseline value. Future, appropriately designed prospective studies with standardized measurements of baseline HbA1C will be necessary to confirm our findings.

Despite these limitations, these data have several strengths. The large sample size of this study population allows stratification of the data by HbA1C levels in order to identify the complex, non-linear relationship between HbA1C and mortality. Although this study was not a prospectively designed study to test the association between HbA1C and outcomes in HF patients, the observational study highlights an association seen in a “real-world” setting of clinical care. Finally, our study provides timely data in light of the recently published ACCORD results which demonstrated a previously unrecognized harm of strategy of tight glycemic control in high-risk patients with type 2 diabetes (30).

Conclusion

In a large national cohort of patients with diabetes and established HF, we demonstrate that the association between levels of HbA1C and mortality appears U-shaped, with increased risk of death at both higher and lower HbA1C levels when compared to modest glucose control (7.1% < HbA1C ≤ 7.8). It is estimated that approximately 20−30%,(8) and perhaps up to 45%,(9) of the nearly 5 million individuals with HF in the US have co-existing diabetes. Therefore, we feel that our data have significant public health implications. We confirm that significantly elevated HbA1C is associated with increased risk in this population and efforts should be made to treat these patients with proven HF therapies and consider glucose lowering therapy. Importantly, we also demonstrate an unexpected hazard with normal or near-normal HbA1C levels in this high-risk population of patients with diabetes and HF. Although this study is an observational study and does not test an active treatment strategy targeting lowering HbA1C levels in diabetic patients with HF, our data are consistent with the ACCORD trial data and demonstrate that low HbA1C levels in this high-risk population are associated with increased risk of death. Future appropriately designed studies are necessary to confirm our findings and expand on potential mechanisms contributing to the increased hazard.

Acknowledgements

The authors thank the Office of Quality and Performance of the Veterans Health Administration for Providing External Peer Review Program data. The views expressed in this article are those of the authors and do not necessarily represent those of the Department of Veteran Affairs.

This study was supported in part by VA Health Services Research & Development Service grant # IIR 02-082-1 (to Dr. Deswal). Dr. Aguilar is a recipient of a NIH Mentored Career Development Award (5K01-HL092585-02).

Abbreviations

HF

Heart failure

HbA1C

Glycosylated hemoglobin

VA

Veteran Affairs

EPRP

External Peer Review Program

COPD

Chronic obstructive pulmonary disease

GFR

Glomerular Filtration Rate

ANOVA

Analysis of Variances

Q

Quintile

LVEF

Left ventricular ejection fraction

BMI

Body mass index

Footnotes

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No conflicts of interest

References

  • 1.Hunt SA, Abraham WT, Chin MH, et al. ACC/AHA 2005 Guideline Update for the Diagnosis and Management of Chronic Heart Failure in the Adult: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Update the 2001 Guidelines for the Evaluation and Management of Heart Failure): developed in collaboration with the American College of Chest Physicians and the International Society for Heart and Lung Transplantation: endorsed by the Heart Rhythm Society. Circulation. 2005;112:e154–235. doi: 10.1161/CIRCULATIONAHA.105.167586. [DOI] [PubMed] [Google Scholar]
  • 2.Mokdad AH, Ford ES, Bowman BA, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA. 2003;289:76–9. doi: 10.1001/jama.289.1.76. [DOI] [PubMed] [Google Scholar]
  • 3.Diabetes mellitus: a major risk factor for cardiovascular disease. A joint editorial statement by the American Diabetes Association; The National Heart, Lung, and Blood Institute; The Juvenile Diabetes Foundation International; The National Institute of Diabetes and Digestive and Kidney Diseases; and The American Heart Association. Circulation. 1999;100:1132–3. doi: 10.1161/01.cir.100.10.1132. [DOI] [PubMed] [Google Scholar]
  • 4.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;35:1628–37. doi: 10.1016/s0735-1097(00)00582-9. [DOI] [PubMed] [Google Scholar]
  • 5.Kannel WB, McGee DL. Diabetes and cardiovascular disease. The Framingham study. JAMA. 1979;241:2035–8. doi: 10.1001/jama.241.19.2035. [DOI] [PubMed] [Google Scholar]
  • 6.Nichols GA, Gullion CM, Koro CE, Ephross SA, Brown JB. The incidence of congestive heart failure in type 2 diabetes: an update. Diabetes Care. 2004;27:1879–84. doi: 10.2337/diacare.27.8.1879. [DOI] [PubMed] [Google Scholar]
  • 7.Kostis JB, Sanders M. The association of heart failure with insulin resistance and the development of type 2 diabetes. Am J Hypertens. 2005;18:731–7. doi: 10.1016/j.amjhyper.2004.11.038. [DOI] [PubMed] [Google Scholar]
  • 8.MacDonald MR, Petrie MC, Hawkins NM, et al. Diabetes, left ventricular systolic dysfunction, and chronic heart failure. Eur Heart J. 2008;29:1224–40. doi: 10.1093/eurheartj/ehn156. [DOI] [PubMed] [Google Scholar]
  • 9.Adams KF, Jr., Fonarow GC, Emerman CL, et al. Characteristics and outcomes of patients hospitalized for heart failure in the United States: rationale, design, and preliminary observations from the first 100,000 cases in the Acute Decompensated Heart Failure National Registry (ADHERE). Am Heart J. 2005;149:209–16. doi: 10.1016/j.ahj.2004.08.005. [DOI] [PubMed] [Google Scholar]
  • 10.From AM, Leibson CL, Bursi F, et al. Diabetes in heart failure: prevalence and impact on outcome in the population. Am J Med. 2006;119:591–9. doi: 10.1016/j.amjmed.2006.05.024. [DOI] [PubMed] [Google Scholar]
  • 11.MacDonald MR, Petrie MC, Varyani F, et al. Impact of diabetes on outcomes in patients with low and preserved ejection fraction heart failure: an analysis of the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM) programme. Eur Heart J. 2008;29:1377–85. doi: 10.1093/eurheartj/ehn153. [DOI] [PubMed] [Google Scholar]
  • 12.Murcia AM, Hennekens CH, Lamas GA, et al. Impact of diabetes on mortality in patients with myocardial infarction and left ventricular dysfunction. Arch Intern Med. 2004;164:2273–9. doi: 10.1001/archinte.164.20.2273. [DOI] [PubMed] [Google Scholar]
  • 13.Goldstein DE, Little RR, Lorenz RA, et al. Tests of glycemia in diabetes. Diabetes Care. 2004;27:1761–73. doi: 10.2337/diacare.27.7.1761. [DOI] [PubMed] [Google Scholar]
  • 14.Standards of medical care in diabetes--2008. Diabetes Care. 2008;31(Suppl 1):S12–54. doi: 10.2337/dc08-S012. [DOI] [PubMed] [Google Scholar]
  • 15.Selvin E, Coresh J, Golden SH, Brancati FL, Folsom AR, Steffes MW. Glycemic control and coronary heart disease risk in persons with and without diabetes: the atherosclerosis risk in communities study. Arch Intern Med. 2005;165:1910–6. doi: 10.1001/archinte.165.16.1910. [DOI] [PubMed] [Google Scholar]
  • 16.Iribarren C, Karter AJ, Go AS, et al. Glycemic control and heart failure among adult patients with diabetes. Circulation. 2001;103:2668–73. doi: 10.1161/01.cir.103.22.2668. [DOI] [PubMed] [Google Scholar]
  • 17.Eshaghian S, Horwich TB, Fonarow GC. An unexpected inverse relationship between HbA1c levels and mortality in patients with diabetes and advanced systolic heart failure. Am Heart J. 2006;151:91. doi: 10.1016/j.ahj.2005.10.008. [DOI] [PubMed] [Google Scholar]
  • 18.Gerstein HC, Swedberg K, Carlsson J, et al. The hemoglobin A1c level as a progressive risk factor for cardiovascular death, hospitalization for heart failure, or death in patients with chronic heart failure: an analysis of the Candesartan in Heart failure: Assessment of Reduction in Mortality and Morbidity (CHARM) program. Arch Intern Med. 2008;168:1699–704. doi: 10.1001/archinte.168.15.1699. [DOI] [PubMed] [Google Scholar]
  • 19.Jha AK, Perlin JB, Kizer KW, Dudley RA. Effect of the transformation of the Veterans Affairs Health Care System on the quality of care. N Engl J Med. 2003;348:2218–27. doi: 10.1056/NEJMsa021899. [DOI] [PubMed] [Google Scholar]
  • 20.Deswal A, Petersen NJ, Urbauer DL, Wright SM, Beyth R. Racial variations in quality of care and outcomes in an ambulatory heart failure cohort. Am Heart J. 2006;152:348–354. doi: 10.1016/j.ahj.2005.12.004. [DOI] [PubMed] [Google Scholar]
  • 21.Levey AS, Coresh J, Greene T, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145:247–54. doi: 10.7326/0003-4819-145-4-200608150-00004. [DOI] [PubMed] [Google Scholar]
  • 22.Williams SB, Goldfine AB, Timimi FK, et al. Acute hyperglycemia attenuates endothelium-dependent vasodilation in humans in vivo. Circulation. 1998;97:1695–701. doi: 10.1161/01.cir.97.17.1695. [DOI] [PubMed] [Google Scholar]
  • 23.Bojunga J, Nowak D, Mitrou PS, Hoelzer D, Zeuzem S, Chow KU. Antioxidative treatment prevents activation of death-receptor- and mitochondrion-dependent apoptosis in the hearts of diabetic rats. Diabetologia. 2004;47:2072–80. doi: 10.1007/s00125-004-1572-7. [DOI] [PubMed] [Google Scholar]
  • 24.Way KJ, Katai N, King GL. Protein kinase C and the development of diabetic vascular complications. Diabet Med. 2001;18:945–59. doi: 10.1046/j.0742-3071.2001.00638.x. [DOI] [PubMed] [Google Scholar]
  • 25.van Heerebeek L, Hamdani N, Handoko ML, et al. Diastolic stiffness of the failing diabetic heart: importance of fibrosis, advanced glycation end products, and myocyte resting tension. Circulation. 2008;117:43–51. doi: 10.1161/CIRCULATIONAHA.107.728550. [DOI] [PubMed] [Google Scholar]
  • 26.Goldin A, Beckman JA, Schmidt AM, Creager MA. Advanced glycation end products: sparking the development of diabetic vascular injury. Circulation. 2006;114:597–605. doi: 10.1161/CIRCULATIONAHA.106.621854. [DOI] [PubMed] [Google Scholar]
  • 27.Kass DA. Getting better without AGE: new insights into the diabetic heart. Circ Res. 2003;92:704–6. doi: 10.1161/01.RES.0000069362.52165.C9. [DOI] [PubMed] [Google Scholar]
  • 28.Witteles RM, Fowler MB. Insulin-resistant cardiomyopathy clinical evidence, mechanisms, and treatment options. J Am Coll Cardiol. 2008;51:93–102. doi: 10.1016/j.jacc.2007.10.021. [DOI] [PubMed] [Google Scholar]
  • 29.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;87:2246–52. doi: 10.1172/JCI115260. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Gerstein HC, Miller ME, Byington RP, et al. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med. 2008;358:2545–59. doi: 10.1056/NEJMoa0802743. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Kalantar-Zadeh K, Block G, Horwich T, Fonarow GC. Reverse epidemiology of conventional cardiovascular risk factors in patients with chronic heart failure. J Am Coll Cardiol. 2004;43:1439–44. doi: 10.1016/j.jacc.2003.11.039. [DOI] [PubMed] [Google Scholar]
  • 32.Kopple JD. The phenomenon of altered risk factor patterns or reverse epidemiology in persons with advanced chronic kidney failure. Am J Clin Nutr. 2005;81:1257–66. doi: 10.1093/ajcn/81.6.1257. [DOI] [PubMed] [Google Scholar]

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