Insulin Resistance and Risk of Congestive Heart Failure FREE

Congestive heart failure (CHF) is a major cause of morbidity and mortality. The age-adjusted mortality for patients with CHF is 4 to 8 times that of the general population.¹ The predominant causes of heart failure are hypertension and coronary heart disease. Other established risk factors for CHF include left ventricular hypertrophy (LVH), valvular heart disease, diabetes, cigarette smoking, obesity, and dyslipidemia.^1- 4

Diabetes as a predictor of subsequent CHF was first described in the Framingham Heart Study 3 decades ago,⁵ and the disease is frequently cited as a risk factor for CHF.^1- 2,6- 7 Yet, more detailed characterizations of the association between diabetes and subsequent CHF are still lacking. In recent years, associations between diabetes or impaired glucose regulation and altered left ventricular geometry and function have been reported.^8- 10 Furthermore, in patients with manifest CHF, insulin resistance is associated with more severe disease and a worse prognosis,^11- 13 but insulin resistance has not been investigated as a predictor of CHF. Obesity is a more recently described risk factor for CHF^3,6- 7 and is also associated with changes in left ventricular geometry and function.¹⁴ Abdominal obesity is closely associated with insulin resistance and manifest diabetes.¹⁵

We hence hypothesized that insulin resistance may predict CHF and may provide the link between obesity and CHF. Our primary aim was to analyze measures of insulin sensitivity (including euglycemic insulin clamp glucose disposal rate) and secretion as predictors of CHF incidence in a community-based sample of elderly men, adjusting for diabetes and other traditional risk factors for CHF. Our secondary aim was to analyze if the previously described association between obesity and CHF may be mediated by insulin resistance.

The study is based on the Uppsala Longitudinal Study of Adult Men cohort (http://www.pubcare.uu.se/ULSAM/), a health investigation focusing on identifying metabolic risk factors for cardiovascular disease, to which all 50-year-old men living in Uppsala, Sweden, in 1970-1974 were invited. Of these, 2322 (82%) participated in the investigation.¹⁶ The cohort was reinvestigated 20 years later (1990-1995, ie, the baseline of the present study). Of the 1681 available 70-year-old men invited to the follow-up investigation, 1221 (73%) attended. For the present study, 20 participants were excluded due to a previous diagnosis of CHF and 14 due to a diagnosis of valvular disease in the hospital discharge register at baseline. Thus, 1187 men were eligible for the present investigation. We examined a subsample of 1061 nondiabetic men after exclusion of all participants with diabetes at baseline (n = 126). Furthermore, we examined a subsample of 1034 nonobese men after exclusion of all men with body mass index (BMI) (calculated as weight in kilograms divided by the square of height in meters) greater than 30 at baseline (n = 153) and another subsample of 433 normal-weight men after exclusion of all men with BMI greater than 25 at baseline (n = 754). All participants gave written informed consent, and the ethics committee of Uppsala University approved the study.

Examinations performed when the participants were 70 years of age included a medical examination, a questionnaire, blood sampling (after an overnight fast), supine blood pressure measurement, anthropometric measurements, a euglycemic insulin clamp, an oral glucose tolerance test (OGTT), and measurement of insulin, proinsulin, and lipid levels as previously described.^10,17 Insulin sensitivity was determined using the euglycemic insulin clamp technique, according to DeFronzo et al,¹⁸ with a slight modification: insulin was infused at a constant rate of 56 instead of 40 mU/(min × m²) to achieve nearly complete suppression of hepatic glucose output.¹⁹ Glucose disposal rate, representing insulin sensitivity, was calculated as the amount of glucose taken up during the last 60 minutes of the clamp procedure and is presented in mg/kg of body weight per minute. An OGTT was performed by measuring the concentrations of plasma glucose and immunoreactive insulin immediately before and 120 minutes after ingestion of 75 g of anhydrous dextrose. The OGTT and the clamp procedure were performed at least 1 week apart. The concentrations of intact and 32-33 split proinsulin were analyzed using a 2-site immunometric assay technique.²⁰ Specific insulin concentrations were determined using a chemiluminescent immunoenzymatic assay. Homeostasis model assessment insulin resistance index was calculated as fasting insulin concentration × fasting glucose concentration/22.5.²¹

Blood pressure was measured in the supine position after resting for 10 minutes. The values were recorded twice to the nearest even value, and the means of the 2 values were given. The presence of hypertension at baseline was defined as systolic blood pressure at least 140 mm Hg and/or diastolic blood pressure at least 90 mm Hg, and/or use of antihypertensive medication. At baseline, 46% of patients with hypertension were treated with antihypertensive medication. The presence of diabetes at baseline was defined as fasting plasma glucose level of 126.1 mg/dL (7.0 mmol/L) or more and/or the use of oral hypoglycemic agents or insulin.²² Electrocardiographic LVH was defined as high-amplitude R waves according to the revised Minnesota code²³ together with left ventricular strain pattern.⁴ Coding of smoking was based on interview reports, and coding of demographic data was based on the questionnaire. The Swedish hospital discharge register was used to assess the presence of valvular disease (International Classification of Diseases, Ninth Revision [ICD-9] codes 394-397 and 424 or ICD-10 codes I05-I08 and I34-I37) and prior myocardial infarction (MI) (ICD-9 code 410 or ICD-10 code I21). The precision of the diagnosis of MI in the discharge register is high.^24- 25

The participants had a median follow-up time of 8.9 years (range, 0.01-11.4 years), contributing to 9899 person-years at risk. One hundred thirty-two men had a hospital discharge register diagnosis of heart failure between the age 70 baseline and the censor date at the end of 2002. As a possible diagnosis of heart failure, we considered ICD heart failure codes 428 (ICD-9) and I50 (ICD-10) and hypertensive heart disease with heart failure, I11.0 (ICD-10). The medical records from the hospitalization were reviewed by 2 physicians (E.I. and L.L.) blinded to the baseline data, who classified the cases as definite, questionable, or miscoded. The classification relied on the definition proposed by the European Society of Cardiology,²⁶ and the review process has been described extensively.²⁷ After this validation, 104 definitive cases of heart failure were included in the total cohort, 87 cases in the subsample without diabetes, and 80 cases in the subsample without obesity. None of the participants was lost to follow-up.

All analyses were defined a priori. Data are presented as mean (SD) or percentage. Logarithmic transformation was performed to achieve normal distribution if necessary. The residuals of all regression analyses were examined and found to be normally distributed. Proportionality of hazards was confirmed by visually examining Nelson-Aalen curves. We examined incidence rates in quartiles of all continuous independent variables, and no obvious deviations from linearity were observed. All variables were treated as continuous, except for prior acute MI, hypertension, diabetes, electrocardiographic LVH, smoking, and interim MI, which were treated as dichotomous. The prognostic values for CHF incidence of a 1-SD increase in the continuous variables, or a transfer from one level to another of the dichotomous variables, were investigated with Cox proportional hazards analyses.

We investigated the independent variables in 5 sets of models in a hierarchical fashion: unadjusted; adjusted for diabetes at baseline; adjusted for diabetes plus other established risk factors for CHF (prior acute MI, hypertension, electrocardiographic LVH, smoking, and serum cholesterol level) determined at baseline; adjusted for diabetes and established risk factors, plus interim MI during the follow-up period; and adjusted for diabetes and established risk factors, plus clamp glucose disposal rate (to examine whether the obesity measures remained predictors of CHF independent of the criterion standard measure of insulin sensitivity). The models were repeated in a subsample excluding all participants with diabetes at baseline and in 2 subsamples excluding all men with BMI greater than 30 and BMI greater than 25 at baseline, respectively. Pearson correlation coefficients were examined to evaluate the correlations between variables reflecting glucose metabolism and those reflecting obesity. In accordance with our a priori analysis plan, missing data were handled such that only participants missing data on a covariate needed for a particular model were excluded from the analyses, to maximize the statistical power. To rule out an effect modification by established risk factors on the relation of insulin sensitivity to CHF, we investigated interaction terms of each of the established risk factors and clamp glucose disposal rate. Two-tailed 95% confidence intervals (CIs) and P values were given, with P<.05 regarded as significant (P values not shown). Analyses were performed using STATA version 8.2 (Stata Corp, College Station, Tex).

One hundred four participants developed CHF during follow-up, and the incidence rate was 10.5 per 1000 person-years at risk. Table 1 shows the clinical characteristics at baseline.

In unadjusted Cox proportional hazards analyses, all examined variables reflecting impaired glucose regulation and obesity were significant predictors of heart failure incidence (Table 2, second column). Incidence rates by quartiles of clamp glucose disposal rate are shown in the Figure. When adjusting for the presence of diabetes at baseline, the following variables remained significant: clamp glucose disposal rate; OGTT 2-hour glucose level; fasting levels of insulin, proinsulin, and 32-33 split proinsulin; BMI; and waist circumference (Table 2, third column). When adjusting also for other established baseline risk factors for CHF (prior acute MI, hypertension, diabetes, electrocardiographic LVH, smoking, and serum cholesterol level), the significant independent predictors of subsequent CHF in separate models were clamp glucose disposal rate, OGTT 2-hour glucose level, fasting proinsulin level, BMI, and waist circumference (Table 2, last column). These 5 variables each remained significant predictors of subsequent CHF, with essentially the same point estimates and CIs, when adding interim MI during follow-up to the covariates (Table 3, second column).

In unadjusted Cox proportional hazards analyses in the subsample excluding participants with diabetes, the significant predictors of CHF incidence were clamp glucose disposal rate, OGTT 2-hour glucose level, fasting levels of proinsulin and 32-33 split proinsulin, BMI, and waist circumference (Table 4, second column). When adjusting for established risk factors for CHF (prior acute MI, hypertension, electrocardiographic LVH, smoking, and serum cholesterol level), the following variables remained significant predictors of CHF in separate models: clamp glucose disposal rate, fasting levels of proinsulin and 32-33 split proinsulin, BMI, and waist circumference (Table 4, last column). These variables, except for fasting 32-33 split proinsulin level, remained significant predictors of subsequent CHF, with essentially the same CIs and point estimates, when adding interim MI to the covariates (Table 3, last column).

When repeating the unadjusted Cox proportional hazards analyses in the subsample of nonobese men, the significant predictors of CHF incidence were clamp glucose disposal rate, fasting glucose level, OGTT 2-hour glucose level, fasting proinsulin level, BMI, and waist circumference (Table 5, second column). When adjusting for the presence of diabetes, the following variables remained significant: clamp glucose disposal rate, OGTT 2-hour glucose level, BMI, and waist circumference (Table 5, third column). When adjusting also for other established risk factors for CHF (prior acute MI, hypertension, diabetes, electrocardiographic LVH, smoking, and serum cholesterol level), only clamp glucose disposal rate remained a significant predictor of CHF (Table 5, last column). Clamp glucose disposal rate remained a significant predictor of subsequent CHF in this subsample also when adjusting for interim MI as well as diabetes plus established risk factors (hazard ratio [HR], 0.73; 95% CI, 0.55-0.97). We also examined a subsample of normal-weight men (excluding all participants with BMI >25 [n = 754]), but this left us with a sample too small (433 participants, 23 cases) to draw any firm conclusions. Nevertheless, the point estimates for clamp glucose disposal rate remained similar but with wider CIs due to low statistical power (HR, 0.78; 95% CI, 0.51-1.18, in the unadjusted model and HR, 0.75; 95% CI, 0.45-1.24, in the models adjusted for diabetes plus established risk factors).

The variables describing impaired glucose regulation and obesity were highly correlated (Pearson r = −0.60, P<.001 for clamp glucose disposal rate vs both BMI and waist circumference). In the models including obesity variables, diabetes plus established risk factors, and clamp glucose disposal rate, the obesity variables were no longer significant, whereas clamp glucose disposal rate remained significant (Table 6). When performing the same analyses in the subsample excluding participants with diabetes at baseline and in the subsample of nonobese men, the same patterns were observed but with larger CIs, rendering some associations borderline significant (Table 6). None of the interaction terms were significant.

In this community-based sample of elderly men free of CHF and valvular disease at baseline, insulin resistance predicted CHF incidence independently of diabetes and other established risk factors for CHF. Furthermore, our observations indicate that the previously described association between obesity and subsequent CHF may be mediated partly by insulin resistance.

Several previous longitudinal studies have shown an association between diabetes and CHF.^1- 2,5- 7 In the present study, clamp glucose disposal rate and fasting proinsulin level, mainly reflecting insulin resistance, were the strongest glucometabolic predictors of CHF, both when adjusting for diabetes and in a subsample without diabetes. To our knowledge, this is the first study to demonstrate a relation between milder states of impaired glucose regulation and CHF incidence. Because information about diabetes incidence during follow-up was not collected in a systematic manner, it is possible that impaired glucose regulation at baseline was a sign of impending diabetes, which is a known risk factor for CHF. Still, we show that impaired glucose regulation in healthy participants without diabetes or obesity at baseline is a strong predictor of subsequent CHF, independent of established risk factors. Our observations may indicate that the risk for CHF is already increased in the long subclinical phase of impaired glucose regulation that precedes clinically manifest diabetes.

In previous studies, signs of impaired glucose regulation have been related to both left ventricular systolic²⁸ and diastolic²⁹ dysfunction and left ventricular remodeling.^8- 10 There are numerous possible explanations for the observed relation of insulin resistance to CHF incidence: (1) The formation of advanced glycosylation end products is greatly accelerated in patients with diabetes,³⁰ which in the myocardium leads to increased collagen cross-linking and myocardial stiffness.³¹ Ventricular function can be improved and myocardial stiffness reversed in diabetic dogs when they are treated with a collagen cross-link breaker such as metformin.³¹ (2) Insulin may act as a growth factor in the myocardium, which is supported by the experimental observation that sustained hyperinsulinemia leads to increased myocardial mass and decreased cardiac output in rats.³² (3) Hyperinsulinemia leads to sodium retention,³³ which may lead to decompensation in persons with otherwise subclinical myocardial dysfunction due to volume expansion. (4) Hyperinsulinemia also leads to sympathetic nervous system activation,³⁴ which is a presumed causal factor for CHF.^1,35 (5) Insulin resistance is related to an increased pressor response to angiotensin II³⁶ and has recently been demonstrated to increase the stimulating effects of angiotensin II on cellular hypertrophy and collagen production³⁷ in individuals with hypertension, leading to myocardial hypertrophy and fibrosis³⁵ and likely subsequent CHF.

Obesity as a risk factor for CHF has been established within the last decade.^3,6- 7 In the present study, BMI and waist circumference were strong predictors of CHF independently of established risk factors for CHF. This demonstrates that both truncal and overall obesity increase the risk of CHF to about the same degree. However, as obesity is also strongly associated with diabetes and insulin resistance,¹⁵ we investigated whether the relation between obesity and CHF may be mediated by insulin resistance. When clamp glucose disposal rate was included in the multivariable models with BMI and waist circumference, the obesity variables were no longer significant predictors of CHF. This observation would be expected if insulin resistance were in the causal pathway between obesity and CHF, which was our hypothesis. Furthermore, in the subsample of nonobese men, clamp glucose disposal rate was a significant predictor of subsequent CHF independent of established risk factors, whereas the obesity variables were no longer significant predictors of CHF. These findings demonstrate that insulin resistance is a risk factor for CHF independent of both truncal and overall obesity. It may imply either that insulin resistance forgoes obesity in a causal pathway leading to CHF, or simply that the relation of obesity to CHF is circumstantial and that obesity in this case may be regarded as an indicator of the more important trait, insulin resistance. It should be noted that it is not possible from our data to definitely disentangle the causative relations between obesity, insulin resistance, and CHF, but our data do add an important piece of knowledge and should stimulate further research in the area.

The strengths of this study include the large, community-based population, the long follow-up period, and the detailed metabolic characterization of the cohort. To our knowledge, the Uppsala Longitudinal Study of Adult Men cohort is the largest population that has been examined with the criterion standard for measurement of insulin resistance, the euglycemic insulin clamp method. Furthermore, all CHF cases were validated, limiting the inclusion of false-positive cases.

There are some limitations to this study. As we only examined men of the same age with a similar ethnic background, this study has an unknown generalizability to women or other age and ethnic groups. On the other hand, we circumvent the powerful effects of age on CHF incidence. Since the CHF diagnosis was based on a review of medical records, it was not possible to distinguish between systolic and diastolic heart failure because echocardiography was not available at the time of diagnosis for many of the cases. Thus, in our material it is not possible to examine whether the impact of insulin resistance is different on systolic vs diastolic heart failure. Finally, as noted above, we do not have information on diagnosis of diabetes during follow-up.

Insulin resistance predicted CHF incidence independently of established risk factors in our large community-based sample of elderly men. The previously described association between obesity and subsequent CHF may be mediated largely by insulin resistance. Further studies are needed to confirm our findings.