Conceived and designed the experiments: UCB CL KGP AWL. Performed the experiments: ML RS MG AB FvZ JT MR CRB BG. Analyzed the data: ML RS MG AB FvZ JT MR CRB BG. Wrote the paper: UCB CL KGP AWL. Revised the manuscript critically for important intellectual content: ML RS MG AB FvZ JT MR CRB BG.
Atherosclerosis is the primary cause of coronary artery disease (CAD). There is increasing recognition that lesion composition rather than size determines the acute complications of atherosclerotic disease. Low serum adiponectin levels were reported to be associated with coronary artery disease and future incidence of acute coronary syndrome (ACS). The impact of adiponectin on lesion composition still remains to be determined.
We measured serum adiponectin levels in 303 patients with stable typical or atypical chest pain, who underwent dual-source multi-slice CT-angiography to exclude coronary artery stenosis. Atherosclerotic plaques were classified as calcified, mixed or non-calcified. In bivariate analysis adiponectin levels were inversely correlated with total coronary plaque burden (r = −0.21, p = 0.0004), mixed (r = −0.20, p = 0.0007) and non-calcified plaques (r = −0.18, p = 0.003). No correlation was seen with calcified plaques (r = −0.05, p = 0.39). In a fully adjusted multivariate model adiponectin levels remained predictive of total plaque burden (estimate: −0.036, 95%CI: −0.052 to −0.020, p<0.0001), mixed (estimate: −0.087, 95%CI: −0.132 to −0.042, p = 0.0001) and non-calcified plaques (estimate: −0.076, 95%CI: −0.115 to −0.038, p = 0.0001). Adiponectin levels were not associated with calcified plaques (estimate: −0.021, 95% CI: −0.043 to −0.001, p = 0.06). Since the majority of coronary plaques was calcified, adiponectin levels account for only 3% of the variability in total plaque number. In contrast, adiponectin accounts for approximately 20% of the variability in mixed and non-calcified plaque burden.
Adiponectin levels predict mixed and non-calcified coronary atherosclerotic plaque burden. Low adiponectin levels may contribute to coronary plaque vulnerability and may thus play a role in the pathophysiology of ACS.
Atherosclerosis is the primary cause of coronary artery disease (CAD), one of the most common causes of illness and death worldwide. There is increasing recognition that lesion composition rather than size determines the acute complications of atherosclerotic disease in humans. Several studies suggested that thin-cap fibroatheroma (non-obstructive plaques) are prone to rupture and result in acute coronary artery occlusions
Initiation and progression of the atherosclerotic lesion are highly complex processes, and many aspects of atherogenesis remain incompletely understood. Ectopic visceral adipose tissue was linked to the pathogenesis of atherosclerosis due to secretion of a multitude of pro- and anti-atherogenic cytokines and adipokines
Adiponectin is the most abundant adipokine produced by adipose tissue. Serum levels of adiponectin are markedly decreased in patients with visceral obesity and states of insulin resistance such as non-alcoholic fatty liver disease and type 2 diabetes
The angiographic assessment of coronary luminal stenosis has been considered a surrogate marker of the severity of atherosclerosis. However, coronary angiography has low predictive value to assess atherosclerotic plaque burden or to predict acute coronary syndrome events
Baseline characteristics of the study population are shown in
| Characteristics | n = 303 | |
| Age (yrs) | 63 (55–70) | |
| Sex | male | 202 |
| female | 101 | |
| Body mass index (kg/m2) | 26.2 (24.1–29.0) | |
| Hypertension |
yes | 147 |
| no | 126 | |
| Diabetes mellitus |
yes | 20 |
| no | 253 | |
| Smoker |
yes | 42 |
| no | 230 | |
| Family history of CAD |
yes | 77 |
| no | 195 | |
| Laboratory profile | LDL-cholesterol (mg/dl) | 122 (95–149) |
| HDL-cholesterol (mg/dl) | 52 (44–59) | |
| Triglycerides (mg/dl) | 143 (106–207) | |
| hsCRP (mg/dl) | 0.23 (0.05–0.54) | |
| Adiponectin (µg/ml) | 5.1 (3.3–7.8) (range 0.2–23.4) | |
| Medical treatment |
Statin | 120 |
| Asa, Plavix or Marcumar | 170 | |
| Betablocker | 173 | |
| ACE-I or ARB | 138 | |
| Diuretics | 87 | |
| Insulin or OAD | 16 | |
| Pericardial adipose tissue volume (ml) |
190 (132–259) | |
| Number of coronary artery plaques (total) |
3 (1–6) (range 0–26) | |
| Number of calcified plaques | 1 (0–3) (range 0–22) | |
| Number of mixed plaques | 0 (0–1) (range 0–10) | |
| Number of non-calcified plaques | 0 (0–2) (range 0–9) |
Values are presented as n or median (interquartile range).
History of diabetes and hypertension is known in 273 patients.
History of smoking and family history of CAD is known in 272 patients.
Medication is known in 258 patients.
Adequate image quality for evaluation of PAT volume was obtained in 287 patients.
Adequate image quality for evaluation of coronary plaques was obtained in 281 patients.
In 60 patients no coronary plaques could be detected. The median number of coronary plaques was 3 (IQR: 1–6; range: 0–26). The median numbers of calcified, mixed and non-calcified plaques were 1 (IQR: 0–3; range: 0–22, number of patients with calcified plaques: 154), 0 (IQR: 0–1; range: 0–10, number of patients with mixed plaques: 102) and 0 (IQR: 0–2; range: 0–9, number of patients with non-calcified plaques: 126), respectively.
Serum adiponectin levels were positively correlated with HDL-cholesterol (HDL-C) (r = 0.32, p<0.0001) and age (r = 0.16, p = 0.005), yet negatively correlated with body mass index (BMI) (r = −0.26, p<0.0001) and triglycerides (r = −0.32, p<0.0001) (
| Characteristics | p | ||
| Age | 0.16 | 0.005 | |
| Sex | Male | 4.7 (2.9–6.6) | <0.0001 |
| Female | 6.9 (4.1–10.1) | ||
| Body mass index (kg/m2) | −0.26 | <0.0001 | |
| Actual hypertension | Yes | 5.0 (2.9–7.5) | 0.13 |
| No | 5.1 (3.4–8.1) | ||
| History of diabetes mellitus | Yes | 4.2 (2.2–8.3) | 0.37 |
| No | 5.0 (3.1–7.7) | ||
| Actual smoker | Yes | 3.9 (2.4–5.7) | 0.02 |
| No | 5.3 (3.2–7.9) | ||
| Family history of CAD | Yes | 4.3 (2.5–7.5) | 0.05 |
| No | 5.3 (3.4–7.9) | ||
| LDL-cholesterol (mg/dl) | 0.09 | 0.11 | |
| HDL-cholesterol (mg/dl) | 0.32 | <0.0001 | |
| Triglycerides (mg/dl) | −0.32 | <0.0001 | |
| High-sensitivity CRP (mg/dl) | −0.04 | 0.49 | |
| Pericardial adipose tissue (ml) | −0.24 | <0.0001 | |
| Total number of coronary artery plaques | −0.21 | 0.0004 | |
| Number of calcified plaques | −0.05 | 0.39 | |
| Number of mixed plaques | −0.20 | 0.0007 | |
| Number of non-calcified plaques | −0.18 | 0.003 |
Values are presented as correlation coefficient or median (interquartile range).
Age (r = 0.38, p<0.0001) and HDL-cholesterol (r = −0.21, p = 0.0003) as internal controls of our data were significantly correlated with total plaque burden in bivariate analysis.
Among demographic characteristics, smoking (p = 0.02) was associated with lower adiponectin levels. No or borderline association was seen with diabetes mellitus (p = 0.37), hypertension (p = 0.13), and family history of CAD (p = 0.05).
In bivariate analysis, adiponectin levels were negatively correlated with total number of coronary artery plaques (r = −0.21, p = 0.0004) (
| Coronary atherosclerotic plaque burden | |||
| estimate | 95% CI | p | |
| Model 1 |
−0.057 | −0.072 to −0.043 | <0.0001 |
| Model 2 |
−0.048 | −0.063 to −0.033 | <0.0001 |
| Model 3 |
−0.041 | −0.056 to −0.025 | <0.0001 |
| Model 4 |
−0.037 | −0.053 to −0.021 | <0.0001 |
| Model 5 |
−0.036 | −0.052 to −0.020 | <0.0001 |
Estimate and 95% CI for decrease in the number of total coronary atherosclerotic plaques for an 1 µg/ml increase in serum adiponectin levels. Full model 5 (including all parameters) is shown in
adjusted for age, sex and BMI.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking and family history of CAD.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides and hsCRP levels.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides, hsCRP levels and medication.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides, hsCRP levels, medication and PAT volume.
Adiponectin was negatively correlated with mixed (r = −0.20, p = 0.0007) and non-calcified plaques (r = −0.18, p = 0.003) (
| Mixed plaques | |||
| estimate | 95% CI | p | |
| Model 1 |
−0.090 | −0.127 to −0.053 | <0.0001 |
| Model 2 |
−0.078 | −0.115 to −0.040 | <0.0001 |
| Model 3 |
−0.080 | −0.120 to −0.041 | <0.0001 |
| Model 4 |
−0.083 | −0.126 to −0.039 | 0.0002 |
| Model 5 |
−0.087 | −0.132 to −0.042 | 0.0001 |
Estimate and 95% CI for decrease in the number of mixed plaques for an 1 µg/ml increase in serum adiponectin levels. Full model 5 (including all parameters) is shown in
adjusted for age, sex and BMI.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking and family history of CAD.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides and hsCRP levels.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides, hsCRP levels and medication.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides, hsCRP levels, medication and PAT volume.
| Non-calcified plaques | |||
| estimate | 95% CI | p | |
| Model 1 |
−0.091 | −0.126 to −0.057 | <0.0001 |
| Model 2 |
−0.082 | −0.117 to −0.048 | <0.0001 |
| Model 3 |
−0.082 | −0.120 to −0.044 | <0.0001 |
| Model 4 |
−0.074 | −0.112 to −0.036 | 0.0001 |
| Model 5 |
−0.076 | −0.115 to −0.038 | 0.0001 |
Estimate and 95% CI for decrease in the number of non-calcified plaques for an 1 µg/ml increase in serum adiponectin levels. Full model 5 (including all parameters) is shown in
adjusted for age, sex and BMI.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking and family history of CAD.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides and hsCRP levels.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides, hsCRP levels and medication.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides, hsCRP levels, medication and PAT volume.
After adjusting for traditional cardiovascular risk factors adiponectin turned out to be significantly associated with calcified plaques (
| Calcified plaques | |||
| estimate | 95% CI | p | |
| Model 1 |
−0.044 | −0.064 to −0.024 | <0.0001 |
| Model 2 |
−0.039 | −0.060 to −0.019 | 0.0001 |
| Model 3 |
−0.026 | −0.047 to −0.005 | 0.01 |
| Model 4 |
−0.022 | −0.044 to −0.000 | 0.05 |
| Model 5 |
−0.021 | −0.043 to 0.001 | 0.06 |
Estimate and 95% CI for decrease in the number of calcified plaques for an 1 µg/ml increase in serum adiponectin levels. Full model 5 (including all parameters) is shown in
adjusted for age, sex and BMI.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking and family history of CAD.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides and hsCRP levels.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides, hsCRP levels and medication.
adjusted for age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides, hsCRP levels, medication and PAT volume.
Our study was designed to examine the relationship of adiponectin with total coronary atherosclerotic plaque burden and atherosclerotic plaque morphology in humans. We demonstrate that 1) adiponectin levels are predictive of total coronary plaque burden, and 2) adiponectin levels are inversely correlated with the number of mixed and non-calcified plaques. No significant correlation was seen with calcified plaques. Our data suggest that the number of mixed and non-calcified plaques decrease by 1 for each 11.5 µg/ml and 13.2 µg/ml increase in serum adiponectin levels, respectively.
Pundziute
Although the exact mechanisms of the negative association between adiponectin and mixed and non-calcified plaques remain to be determined, several possible explanations should be considered. In addition to its beneficial effects on insulin sensitivity and lipid metabolism
The present study has several strengths and limitations. Our data support the concept that adiponectin is an important marker in the pathogenesis of atherosclerosis since adiponectin remains significantly associated with plaque morphology in a fully adjusted multivariate model containing age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides, hsCRP, medication and PAT volume. However, since this was an association study, our study does not establish a causal relationship between adiponectin and coronary plaque morphology. Furthermore, the clinical implications of our results still need to be determined.
There are conflicting epidemiological data regarding the role of adiponectin in atherosclerosis with some studies showing strong inverse associations between adiponectin levels and CAD
In the present study adiponectin is not correlated with calcified plaques, the majority of coronary plaques in our study cohort. Therefore, although statistically highly significant, adiponectin levels are associated with a very modest decrease in total coronary plaque burden accounting for only 3% of the variability in total number of coronary plaques. In contrast, adiponectin accounts for approximately 20% of the variability in mixed and non-calcified plaque burden. Determination of the bioactive high molecular weight form of adiponectin may have possibly resulted in an even stronger association of adiponectin with plaque burden and morphology. Overall, our data emphasize the importance of additional risk factors in the pathogenesis of atherosclerosis.
Storage conditions of blood samples (e.g. storage time and temperature) were suggested to alter adiponectin levels
Finally, our study results may only be applicable to non-diabetic patients. Diabetics were clearly underrepresented in the present study (7% of patients). The small number of diabetic patients may also explain the lack of association between adiponectin levels and diabetes.
In summary, we demonstrate that adiponectin is inversely associated with coronary plaque burden and mixed and non-calcified plaques, suggesting an important role of adiponectin in the pathogenesis of atherosclerosis and possibly in the pathophysiology of acute coronary syndrome.
303 consecutive patients who underwent DSCT-coronary angiography for exclusion of coronary artery stenosis due to stable typical or atypical chest pain, were recruited during 20 consecutive months from March 2006 to October 2007. After providing informed written consent, study subjects were asked to complete a brief questionnaire and have blood drawn. The study protocol was approved by the Ethics Committee of the Ludwig-Maximilians-University Munich, Germany.
CT-coronary angiography was performed using a Siemens Definition scanner (Siemens Medical Solutions, Forchheim, Germany) that uses two X-ray sources for image generation. Tube voltage for CT-angiography was 120 kV for both tubes in patients with a body weight >80 kg and 100 kV for those with a weight <80 kg. Current was 560 mA with modulation, and full current between 30–50% to 80% of the cardiac cycle. Gantry rotation time was 0.33 s, and pitch 0.2–0.44 adapted to the HR. Per rotation 64 slices were generated with a collimation of 0.6 mm, leading to an isotropic voxel resolution of approximately 0.6 mm edge length and 0.2 mm3 volume. Before the scan, nitroglycerine was administered sublingually. A bodyweight-adapted volume of contrast agent (1.25 cm3/kg bodyweight, Ultravist 370, Schering, Berlin, Germany) was injected continuously at a calculated rate to achieve constant injection during 20 s. The scan was started with a delay of 5 s after the density in the aortic root exceeded a density value of 100 HU (bolus tracking). A saline flush (100 cm3 at 5 cm3/s) was applied to maintain a compact bolus. Axial images were reconstructed with 0.75 mm slice thickness and 0.5 mm increment using a medium sharp convolution kernel (B26f) and retrospective ECG gating. The reconstructions were performed in 10% steps over the entire R-R cycle using a single-segment algorithm that utilizes a quarter segment of projection data from both detectors. In atrial fibrillation, data were reconstructed in 50 ms steps.
In the first step, all reconstructed data sets were evaluated at different ECG-phases for diagnostic image quality and the optimal data set was then chosen for analysis. The DSCT datasets were evaluated by two independent investigators blinded to serum adiponectin levels using a dedicated cardiac workstation (Siemens, Leonardo Circulation).
Atherosclerotic plaques were classified as calcified, mixed or non-calcified as described previously
The interobserver agreement of the 2 investigators was 95% for calcified plaques, 94% for mixed plaques, and 93% for non-calcified plaques.
The same images as for the analysis of atherosclerotic plaques were used to determine PAT volume. PAT volume was measured in ml using the volume analysis software tool of the Siemens Leonardo Circulation workstation. PAT volume was determined similar to the method described by Gorter et al.
Blood samples were stored in aliquots at −70°C until analysis (i.e. up to 21 months). Serum levels of adiponectin (µg/ml) were determined with a commercial enzyme-linked immunosorbent assay (R&D, Wiesbaden, Germany). To determine whether storage conditions may have affected adiponectin levels, adiponectin concentrations in aliquots stored for 21 and 33 months at −70°C from 20 patients were measured. Adiponectin levels did not differ significantly between the two timepoints (21 months: 5.6±1.2 (mean±standard deviation) µg/ml versus 33 months: 5.3±1.1 µg/ml; p = 0.45) indicating adequate specimen stability under our storage conditions.
Plasma LDL-C, HDL-C and triglycerides were measured by routine enzymatic methods. Determination of hsCRP levels was performed at the Department of Clinical Chemistry (Campus Grosshadern, University of Munich, Germany).
Statistical analyses were performed using SAS 9.1 (Cary, NC) software. Data are reported as n or median (interquartile range). Spearman correlation and Wilcoxon two-sample test were used in the bivariate analysis of adiponectin with other variables. A generalized linear regression model was used to assess the association of adiponectin serum levels with atherosclerotic plaque burden, number of calcified plaques, mixed plaques or non-calcified plaques adjusted for age, sex, BMI, diabetes, hypertension, family history of CAD, smoking, LDL-C, HDL-C, triglycerides, hsCRP levels, medical treatment (statin, asa, plavix, marcumar, betablockers, ACE inhibitors, angiotensin-receptor blockers, diuretics, oral anti-diabetic drugs, insulin) and PAT volume - possible confounders of adiponectin levels. All tests were two-tailed with a 0.05 type I error rate.
Full adjusted model (model 5) for total number of coronary plaques, mixed, non-calcified and calcified plaques
(0.05 MB DOC)
Click here for additional data file.
We are indebted to Elisabeth Fleischer-Brielmaier, Inge Biller-Friedmann and Kerstin Henze for expert technical assistance.