Low Plasma Adiponectin Levels Predict Progression of Coronary Artery Calcification
Background— Circulating adiponectin levels are lower in men than in women and lower in advanced coronary artery disease, obesity, and type 2 but not type 1 diabetes. However, it is not known whether low adiponectin levels predict development of atherosclerosis independently of other cardiovascular risk factors.
Methods and Results— Progression of coronary artery calcification (CAC) over an average of 2.6 years (range, 1.6 to 3.3) was assessed in a cohort of patients with type 1 diabetes and nondiabetic subjects 19 to 59 years of age. In this nested case-control substudy, plasma adiponectin levels were measured in 101 cases with significant CAC progression and in 205 controls. Controls were oversampled on the basis of age, gender, diabetes status, and presence of baseline CAC. In conditional logistic regression adjusted for baseline CAC volume and other significant predictors of CAC progression, adiponectin levels were inversely related to progression of CAC in diabetic (OR, 0.47; 95% CI, 0.24 to 0.94) and nondiabetic (OR, 0.15; 95% CI, 0.05 to 0.40 for a doubling in adiponectin levels) subjects. Adjustment for additional cardiovascular risk factors did not change this association. In conditional logistic regression models by quartiles of plasma adiponectin levels, the probability value for trend was statistically significant for all participants (P<0.001) and nondiabetic participants (P<0.001) and was borderline for type 1 diabetics (P=0.08).
Conclusions— Low plasma adiponectin levels are associated with progression of CAC in type 1 diabetic and nondiabetic subjects independently of other cardiovascular risk factors.
Received August 17, 2004; revision received November 23, 2004; accepted November 29, 2005.
Adiponectin is a 30-kDa, collagen-like protein synthesized by adipocytes that circulates in human plasma as ≈0.01% of total plasma protein.1 Adiponectin is lower in men than women and in patients with hypertension or type 2 diabetes (T2DM); it correlates negatively with age, body mass index (BMI), insulin resistance, and levels of plasma insulin, triglycerides, glucose, and C-reactive protein (CRP).2–9 Conversely, adiponectin is positively correlated with HDL and has been demonstrated to increase in individuals who lose weight.2,10 Adiponectin accumulates in damaged vascular walls11 and beneficially modulates the endothelial inflammatory response to vascular injury.12,13
Given the relationship between adiponectin and obesity and other cardiovascular risk factors, it has been proposed as a link in the adipose-vascular axis14 and may play a role in the development of atherosclerosis. Indeed, adiponectin has been shown to be lower in patients with advanced coronary artery disease (CAD) than in age-, gender-, and BMI-matched controls.12 Prospective data demonstrating that adiponectin levels are significantly and independently related to development of atherosclerosis or CAD12,15 have been reported in patients with end-stage renal disease (ESRD)16 and in a nested case-control study within the Health Professionals Follow-up Study.17
In contrast to a significant body of evidence linking low adiponectin to insulin resistance and T2DM, little is known about adiponectin levels in type 1 diabetes (T1DM), which is usually not characterized by insulin resistance. In this prospective case-control study nested within a larger cohort study of young adults with and without T1DM,18 change in coronary artery calcium (CAC) volume was used as a marker of atherosclerosis progression over a 2.6-year follow-up period. The purpose of this study was to determine significant and independent predictors of progression of CAC and to evaluate the role of adiponectin levels as a potential novel cardiovascular risk factor.
The Coronary Artery Calcification in Type 1 Diabetes (CACTI) Study is a prospective cohort study designed to assess the development and progression of subclinical CAD in subjects with T1DM and in nondiabetic (non-DM) controls to identify targets for primary prevention of CAD in this population. The study design has been described in detail elsewhere.18 Briefly, the study assessed the extent of CAC in a cohort of 654 T1DM and 765 non-DM subjects (excluding 1 participant diagnosed with T2DM at baseline) 19 to 59 years of age at the baseline in 2000 to 2002. All subjects were asymptomatic for CAD and had no history of CABG, coronary angioplasty, or unstable angina. All participants with diabetes had been diagnosed when <30 years of age or had a diagnosis of T1DM confirmed by an endocrinologist and had been treated with insulin within 1 year of diagnosis. All non-DM participants reported never having been diagnosed with diabetes of any type, including gestational diabetes, and were generally spouses, friends, and neighbors of the cases.
The data presented in this report were collected as part of the follow-up examination of the first 674 members of the CACTI cohort after an average of 2.6 years (range, 1.6 to 3.3 years), which included a nested case-control substudy of 101 participants whose CAC progressed significantly and 205 nonprogressing controls (see below for definition of significant progression and methods for selecting cases and controls).
All patients underwent 2 electron-beam CT (EBCT) scans within 5 minutes without contrast at baseline and 2 scans at follow-up as previously described.18 Images of the entire epicardial system were obtained with an Imatron C-150 Ultrafast CT scanner with a 100-ms exposure. The standard acquisition protocol previously described was used.19 Scanning started from near the lower margin of the bifurcation of the main pulmonary artery. Images were electrocardiographically triggered at 80% of the R-R interval, and 30 to 40 contiguous 3-mm slices were acquired. The volume scores were calculated with the volumetric method, which is based on isotropic interpolation as previously described.20
Definition of CAC Progression
In this study, we chose to define progression as reported by Hokanson et al,21 who noted that bias in the interscan variability of calcium volume scores (CVSs) exists so that the variability increases as levels of coronary calcium increase. If not accounted for, this may lead to overestimation of changes in CVS over time at higher levels of coronary calcium. Alternatively, using percent change in CVS as a potential measure of changes in coronary calcium may underestimate changes at higher levels of coronary calcium. Using paired mean CVS measurements in 1074 subjects who had 2 EBCT scans 5 minutes apart, Hokanson et al21 found that square root transformation of CVS provides a stable estimate of interscan variability across the ranges of coronary calcium observed in the present study, thus allowing investigations of changes in coronary calcium that are not biased by level of coronary calcium. Furthermore, Hokanson et al suggested using a difference between baseline and follow-up square root–transformed CVS of ≥2.5 to signify a significant change in CVS because a change of this magnitude is <1% likely to be due to interscan variability.
Selection of Cases
The baseline and follow-up CVSs were square root transformed, and the difference was calculated for each subject. Individuals were categorized as progressors if the change in square root CVS was >2.5. All subjects who qualified as progressors (n=101) at the time of the nested case-control study were included as cases. Regression of CAC would similarly be defined as a reduction in the square root CVS of >2.5; however, none of the patients in this study experienced regression according to this definition.
Selection of Controls
The remaining 573 subjects were classified as nonprogressors (ie, a change in square root CVS ≤2.5) and were eligible to be selected as controls. Individual matching of controls to each of the cases was not possible with the number of controls available, yet we wanted to choose controls who would provide us with the most valuable information and thus the most efficient analysis. We therefore oversampled controls on the basis of age, gender, diabetes status, and presence of baseline CAC. Because the majority of cases (70 of 101) but only a minority of controls (79 of 573) had measurable CAC at baseline, we selected all such controls for the nested case-control study. We then selected 126 of the remaining 494 controls to be frequency matched to cases within strata defined by diabetes status, gender, and age group (<30, 30 to 39, 40 to 49, ≥50 years). In strata with a sufficient number of controls, we randomly selected twice the number of cases in the stratum. In strata with ≤2 controls per case, we selected all possible controls in that stratum for inclusion in the present study.
After subjects fasted overnight, blood was collected and centrifuged, and separated plasma was stored at 4°C until assayed. Adiponectin was measured in the GCRC core laboratory at the University of Colorado Health Sciences Center (Denver) in duplicate with a commercial radioimmunoassay procedure (Linco Research, Inc). Stored samples (−70°C) from the subjects’ baseline study visit were diluted 1:500 before testing. Intra-assay precision was 7.5% (3.9% per package insert); interassay precision was 8.5%. Results are reported in micrograms per milliliter, with a sensitivity cutoff of 1.0 ng/mL.
Total plasma cholesterol and triglyceride levels were measured with standard enzymatic methods; HDL cholesterol was separated by use of dextran sulfate; and LDL cholesterol was calculated from the Friedewald formula. High-performance liquid chromatography (BioRad variant) was used to measure HbA1c. Plasma glucose was measured with standard hexokinase method. Homocysteine was determined by the Abbot IMX automated procedure. CRP, plasminogen activator inhibitor type 1 (PAI-1), and fibrinogen were measured in the laboratory of Dr Russell Tracy at the University of Vermont. CRP was measured with the BNII nephelometer (Dade Behring) using a particle-enhanced immunonephelometric assay. PAI-1 was done as a 2-site ELISA. Fibrinogen was measured in an automated clot-rate assay with the Sta-r instrument. Urine albumin excretion was determined by radioimmunoassay, and the results of 2 timed overnight urine collections were averaged.
We measured height and weight and calculated BMI. Minimum waist and maximum hip measurements were obtained in duplicate, and the results were averaged. Intra-abdominal fat and subcutaneous fat were assessed with an abdominal CT scan at the L2-L3 levels. The L2-L3 disc space was located by counting the lumbar vertebra, with L1 being the first non–rib-bearing vertebra. A single 6-mm-thick image was obtained through the L2-L3 disc space during suspended respiration. This process was repeated at the L4-L5 disc space. The total intra-abdominal fat volume and subcutaneous fat volume in cubic centimeters were measured with AccuAnalyzer software from AccuImage.
Resting systolic (SBP) and fifth-phase diastolic (DBP) blood pressures were measured 3 times while the patient was seated, and the second and third measurements were averaged.22 Hypertension was defined as current antihypertensive therapy or untreated hypertension (blood pressure ≥140/90 mm Hg) at the time of the study visit.
Insulin resistance was approximated as the inverse of the estimated glucose disposal rate (EGDR),23 calculated according to this formula: where WHR is waist-to-hip ratio. The equation was derived from hyperinsulinemic euglycemic clamps performed in 24 T1DM participants in the Pittsburgh Epidemiology of Diabetes Complications (EDC) Study.
Duration of diabetes was determined by patient self-report. Current and former smoking status was obtained by questionnaire; for smokers, the total number of pack-years was calculated.
Data are presented as arithmetic means and SD for continuous variables (geometric means and ranges for log-transformed variables) and percentages for categorical variables. Because the observed means for controls were dependent on the sampling scheme, least-squares means adjusted to the distribution of age, gender, diabetes status, and presence of baseline CAC for CAC progressors are reported for controls, and differences between cases and controls were evaluated after adjustment for these factors. Conditional logistic regression models for m:n matched data were estimated with SAS PROC PHREG (SAS Institute Inc). To account for the sampling scheme, all models included diabetes status, gender, and presence of baseline coronary calcium as stratification factors and age as a continuous covariate. Continuous variables were examined for a linear relationship with progression of coronary calcium and were transformed when necessary. A square root transformation was used for baseline CVS, and log transformations were used for triglycerides, albumin excretion rate, and adiponectin levels. ORs and 95% CIs for the effect of adiponectin as a continuous variable are reported per doubling in adiponectin levels on the original scale, which is equivalent to a 0.69-U increase on the log scale (or ≈1.4-SD increase on the log scale). Subgroup analyses were performed for diabetic and non-DM patients, and differences in reported ORs between diabetic and non-DM subjects were tested through the use of interaction terms.
To evaluate the relationship between continuous adiponectin levels and CVS progression, we first fit a base model stratified on the matching factors listed previously, including age and log-transformed adiponectin levels as continuous variables. We then fit a series of models that sequentially adjusted for additional a priori sets of cardiovascular risk factors, including baseline CVS, components of the Framingham risk score (SBP, DBP, LDL cholesterol, HDL cholesterol, and smoking status), anthropometric variables (BMI, waist circumference, and intra-abdominal and subcutaneous fat by CT), and novel cardiovascular disease (CVD) risk factors (CRP, homocysteine, serum creatinine, and urinary albumin excretion rate). Next, we sought a parsimonious model that included only those variables that were independently associated with progression of CVS in a stepwise-backward elimination procedure. This model also considered additional risk factors such as triglycerides, apolipoprotein B, PAI-1, fibrinogen, HbA1c, and EGDR. We further explored the relationship between quartile of baseline adiponectin and progression of CVS. We tested for a linear trend in log-transformed adiponectin levels across quartiles using a single ordinal variable, with the log of the median adiponectin level within each quartile as its value. Because there was a concern that residual confounding by baseline CVS could bias our results, we also ran our principal models on the subset of cases and controls with no measurable CAC at baseline.
The study protocol was reviewed and approved by the Colorado Combined Institutional Review Board. Informed consent was obtained from all participants before enrollment.
Baseline characteristics of study participants with significant CVS progression (n=101: 69 T1DM, 32 non-DM) and controls (n=205: 112 T1DM, 93 non-DM) are given in Table 1. Because our study design allowed for a variable number of controls per case, it was not unexpected that cases were older, were more likely to be diabetic, were more often male, and had higher baseline CVS compared with controls. The median CVS among the cases increased from 16.1 to 85.1 mm3 over an average of 2.6 years (range, 1.6 to 3.3 years) of follow-up. After adjustment for age, gender, diabetes status, and presence of baseline CAC (Table 1), CVS progressors had greater risk factors associated with CAD compared with controls, including increased BMI, increased subcutaneous fat, higher blood pressure, and higher plasma levels of homocysteine. Plasma adiponectin levels were 22% higher in controls compared with cases (P<0.001). Plasma adiponectin levels were higher in T1DM participants compared with non-DM participants (2.4±0.5 versus 2.0±0.4 μg/mL, P<0.001).
Table 2 displays the results for the series of conditional logistic regression models adjusting for the a priori set of cardiovascular risk factors, with sequential adjustment for (1) selection factors (gender, diabetes, and presence or absence of baseline CVS, adjusted for age); (2) the above factors and baseline CVS; (3) all the above factors and components of the Framingham risk score (SBP, DBP, LDL, HDL, smoking); (4) all the above factors and measures of overall and central obesity; and (5) all the above factors plus novel CVD risk factors (CRP, homocysteine, serum creatinine and urinary albumin excretion rate). With adjustment for only those factors used to oversample controls for the study (model 1), a doubling of adiponectin levels was associated with a 54% reduction in the odds of progression for all participants (OR, 0.46; 95% CI, 0.30 to 0.72; P<0.001), a 41% reduction in the odds of progression for T1DM participants (OR, 0.59; 95% CI, 0.34 to 1.02; P=0.06), and a 73% reduction in the odds of progression for non-DM participants (OR, 0.27; 95% CI, 0.12 to 0.59; P=0.001). As shown in Table 2, sequential adjustment for additional risk factors (models 2 through 5) strengthened the association between adiponectin and CAC progression, although the OR was significantly more protective in non-DM participants compared with T1DM participants in these models (P<0.05 for interaction between diabetes and adiponectin). After additional risk factors were considered and nonsignificant predictors of CAC progression were eliminated, the reduced model (model 6) included—in addition to age, gender, baseline CVS, and log-transformed adiponectin levels—SBP, BMI, and intra-abdominal fat measures. In this model, the OR for all participants was 0.33 (95% CI, 0.19 to 0.57; P<0.001), indicating that a doubling of adiponectin levels was associated with 67% lower odds of CAC progression. This association was somewhat weaker but still significant in T1DM patients (OR, 0.47; 95% CI, 0.24 to 0.94; P=0.03) compared with non-DM subjects (OR, 0.15; 95% CI, 0.05 to 0.40; P<0.001), although the interaction between adiponectin and diabetes status did not reach statistical significance (P=0.06). Interactions between gender and adiponectin were also assessed, but none were significant (data not shown).
Table 3 presents the results of conditional logistic regression models by quartile of plasma adiponectin level overall and stratified by diabetes status for our principal models. The probability value for trend in log adiponectin for the most parsimonious model (model 6) is highly significant for all participants (P for log trend <0.001) and non-DM participants (P for log trend <0.001) and of borderline significance for T1DM participants (P for log trend=0.08). As shown in the parsimonious model (model 6), the odds of CAC progression were 82% lower for participants in the highest quartile of baseline adiponectin compared with those in the lowest quartile (OR, 0.18; 95% CI, 0.06 to 0.52; P for log trend <0.001) when all participants were analyzed together. In subgroup analyses, the odds of CAC progression were 60% lower (OR, 0.40; 95% CI, 0.11 to 1.41; P for log trend=0.08) and 96% lower (OR, 0.04; 95% CI, 0.01 to 0.27; P for log trend <0.001) in the highest compared with lowest quartile of adiponectin for T1DM and non-DM participants, respectively.
Table 4 repeats our principal models on the subset of cases (n=31) and controls (n=126) with no measurable CAC at baseline. The OR for the most parsimonious model (model 6) was similar to that for all cases and controls as reported in Table 3 (OR, 0.28 [95% CI, 0.12 to 0.63] versus 0.33 [95% CI, 0.19 to 0.57]). This was also found for non-DM participants (11 cases, 53 controls) (OR, 0.17 [95% CI, 0.05 to 0.65] versus 0.15 [95% CI, 0.05 to 0.40]). In T1DM participants (20 cases, 73 controls), adiponectin had a lower OR, although it did not reach statistical significance (OR, 0.32 [95% CI, 0.08 to 1.20; P=0.09] versus 0.47 [95% CI, 0.24 to 0.94]). For this subanalysis of participants with no CVS at baseline, the diabetes–adiponectin interaction was not significant (P=0.52).
In summary, low plasma adiponectin was one of the strongest and the most consistent predictors of short-term progression of subclinical coronary atherosclerosis in asymptomatic men and women, although the protective effect of adiponectin appeared to be less robust in T1DM participants than in non-DM study participants.
The major finding in this study is that low plasma adiponectin levels are associated with progression of CVS in both non-DM and T1DM subjects and in both genders independently of other traditional CVD risk factors. This is the first report demonstrating that plasma adiponectin levels predict progression of coronary atherosclerosis in both a T1DM and non-DM population, adding significantly to previous limited evidence for an inverse relationship between circulating adiponectin levels and CAD.2,15,16 In addition, a recent nested case-control study from the Health Professionals Follow-up Study reported that in men 40 to 75 years of age, high adiponectin levels were associated with a lower risk of myocardial infarction.17
The use of CAC as a marker of CAD rather than traditional end points such as coronary artery stenosis, myocardial infarction, or death is one of the limitations of our study. Although longer follow-up of larger populations is necessary to establish that low adiponectin levels predict development of clinical disease, CAC has generally been accepted as a quantifiable, reliable, noninvasive marker of the extent of coronary atherosclerosis.24 CAC scores (absolute and percentile ranked) and the rate of subclinical progression in calcification as measured by EBCT have been shown to predict both fatal and nonfatal coronary events.25,26 To maximize precision of outcome measurement, 2 scans were performed at both baseline and follow-up, and statistical analyses were performed to render CVS scores with stable variance. The fact that the observed associations between low adiponectin levels and CVS progression were present in both non-DM and T1DM patients and in men and women and did not change in strength with sequential adjustment for multiple CVD risk factors demonstrates the internal validity of the findings. Overall and among non-DM participants with no baseline CVS, the protective effect of adiponectin was similar to that when all baseline CVS scores are included. Few data exist on the measurement of insulin resistance in T1DM subjects; for this study, we used the method with the best supporting data.23 Further study is needed on the degree of insulin resistance in subjects with T1DM and how this can best be measured in epidemiological and clinical studies.
In T1DM participants, however, the OR was lower (0.32 for T1DM participants with no baseline CVS versus 0.47 for T1DM regardless of baseline CVS). Additionally, for T1DM participants, the OR unexpectedly increased in the quartile with the highest adiponectin levels compared with the second and third quartiles. Renal function has been hypothesized to be a determinant of adiponectin levels; therefore, renal function (as measured by serum creatinine) was analyzed by adiponectin quartile. Although serum creatinine was highest in the highest quartile of adiponectin (geometric least-squares means quartiles 1 through 4: 1.23, 1.23, 1.18, and 1.39; P for log trend <0.001), serum creatinine was not significantly associated with CAC progression in our data and thus could not explain the higher-than-expected odds of CAC progression in the highest quartile of adiponectin. Given the small sample size (16 cases, 29 controls in the fourth quartile), this hypothesis requires future study in the full CACTI cohort.
There is a growing body of evidence for the beneficial role of adiponectin in the process of atherosclerosis.2,12,15–17 Proposed mechanisms by which adiponectin exerts protective effects include suppression of tumor necrosis factor-α–induced endothelial adhesion molecule expression, macrophage-to–foam cell transformation, and tumor necrosis factor-α expression in macrophage tissue.9,12,27 Further clinical and mechanistic studies of adiponectin in relationship to CAD are needed. A reciprocal association of CRP and adiponectin has been reported,9 but this association was not noted in the present study, nor was CRP a significant predictor of CAC progression in this study after adjustment for age, gender, diabetes, and baseline CAC. This may suggest that CRP is less proximal to the process of coronary atherosclerosis than adiponectin; however, a longer follow-up is needed to assess whether the predictive value of CRP for coronary events is secondary to its correlation with adiponectin levels. Our data confirm higher levels of adiponectin in T1DM patients compared with age- and BMI-matched controls28,29 or subjects with T2DM. Patients with ESRD are the only other population previously shown to have elevated levels of adiponectin.16 Potential mechanisms of elevated adiponectin levels in T1DM and ESRD patients include diminished adiponectin clearance rates as a result of impaired renal function.7 Additional factors may play a role because adiponectin levels are higher in patients with both ESRD and T1DM compared with those with ESRD and T2DM or non-DM ESRD patients.30 CAD is the main cause of death in T1DM, with 35% mortality caused by CAD in T1DM patients by 55 years of age in contrast to 4% of non-DM women and 8% of men.31 Therefore, the promising role of adiponectin as a predictor of and potential therapeutic target for prevention of CAD deserves further investigation.
In conclusion, low plasma adiponectin levels are associated with progression of subclinical coronary atherosclerosis in people with T1DM and in non-DM subjects independently of other cardiovascular risk factors. These results need to be confirmed in prospective studies of diabetic and non-DM populations, with clinical disease as the primary outcome.
Support for this study was provided by NIH National Heart, Lung and Blood Institute grant R01 HL61753 and DERC Clinical Investigation Core P30 DK57516. Support for Dr Maahs was provided by NIH NIDDK grant T32 DK063687-03. This study was performed at the Adult General Clinical Research Center at the University of Colorado Health Sciences Center supported by the NIH M01 RR00051, at the Barbara Davis Center for Childhood Diabetes (Denver, Colo), and at the Colorado Heart Imaging Center (Denver).
Dr Ehrlich is the medical director of and a shareholder in coronary imaging centers that use electron beam tomography scanners.
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