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Severity of coronary atherosclerosis in patients with a first acute coronary event: a diabetes paradox

Giampaolo Niccoli, Simona Giubilato, Luca Di Vito, Andrea Leo, Nicola Cosentino, Dario Pitocco, Valeria Marco, Giovanni Ghirlanda, Francesco Prati, Filippo Crea
DOI: http://dx.doi.org/10.1093/eurheartj/ehs393 729-741 First published online: 27 November 2012

Abstract

Aims We aimed to compare coronary artery disease (CAD) at the time of a first acute coronary syndrome (ACS) in type II diabetic and non-diabetic patients by coronary angiography and by optical coherence tomography (OCT).

Methods and results Two different patient populations with a first ACS were enrolled for the angiographic (167 patients) and the OCT (72 patients) substudy. Angiographic CAD severity was assessed by Bogaty, Gensini, and Sullivan scores, whereas collateral development towards the culprit vessel was assessed by the Rentrop score. Optical coherence tomography plaque features were evaluated at the site of the minimum lumen area (MLA) and of culprit segment. In the angiographic substudy, at multivariate analysis, diabetes was associated with both the stenosis score and the extent index (P = 0.001). Furthermore, well-developed collateral circulation (Rentrop 2–3) towards the culprit vessel was more frequent in diabetic than in non-diabetic patients (73% vs. 16%, P = 0.001). In the OCT substudy, at MLA site lipid quadrants were less and the lipid arc was smaller in diabetic than in non-diabetic patients (2.3 ± 1.3 vs. 3.0 ± 1.2; P = 0.03 and 198° ± 121° vs. 260° ± 118°; P = 0.03). Furthermore, the most calcified cross-section along the culprit segment had a greater number of calcified quadrants and a wider calcified arc in diabetic than in non-diabetic patients (1.7 ± 1.0 vs. 1.2 ± 0.9; P = 0.03 and 126° ± 95° vs. 81° ± 80°; P = 0.03). Superficial calcified nodules were more frequently found in diabetic than in non-diabetic patients (79 vs. 54%, P = 0.04).

Conclusions In spite of potent pro-inflammatory, pro-oxidant and pro-thrombotic stimuli operating in type II diabetes, diabetic patients exhibit substantially more severe coronary atherosclerosis than non-diabetic patients at the time of a first acute coronary event. Better collateral development towards the culprit vessel, a predominantly calcific plaque phenotype and, probably, yet unknown protective factors operating in diabetic patients may explain these intriguing paradoxical findings.

  • Diabetes
  • Acute coronary syndrome
  • Atherosclerotic burden
  • Collaterals
  • Coronary calcium

See page 715 for the editorial comment on this article (doi:10.1093/eurheartj/ehs441)

Introduction

It is well recognized that patients with type 2 diabetes mellitus (DM) exhibit a higher incidence of acute vascular events and of cardiac death than non-diabetic patients.16

This has been related to the presence of more intense pro-inflammatory, pro-oxidant, and pro-thrombotic stimuli in the former.7 On the basis of these observations, the atherosclerotic burden, at the time of a very first acute coronary event, should be less in diabetic than in non-diabetic patients because of the higher susceptibility to thrombus formation in diabetic patients. Otherwise, one might speculate that protective mechanisms delay a first episode of coronary instability in diabetic patients. The aim of this study was to compare coronary artery disease (CAD) severity and features in type II diabetic and non-diabetic patients by coronary angiography and optical coherence tomography (OCT) at the time of a first acute coronary event.

Methods

Study design

This study comprises an angiographic and an OCT substudy carried out on two different patient populations that were enrolled in two hospitals in Rome (Policlinico Gemelli for the angiographic and OCT substudy and San Giovanni Hospital for the OCT substudy) in a temporal window ranging from December 2009 to June 2012 (Figure 1). Both studies enrolled patients presenting non-ST-elevation acute coronary syndrome (NSTE-ACS), as their first clinical manifestation of CAD.

Figure 1

Flow chart summarizing design of the study and parameters that were collected in the patient cohort.

Angiographic substudy

Patient population

One hundred and sixty-seven consecutive patients undergoing coronary angiography because of NSTE-ACS, as their first clinical manifestation of CAD, and showing obstructive atherosclerosis at angiography admitted at the Policlinico Gemelli (Rome, Italy) between February 2010 and March 2011 were prospectively included in the study. Non-ST-elevation acute coronary syndrome was defined as chest pain at rest in the last 48 h preceding the admission associated with evidence of transient ST-segment depression on 12-lead ECG and normal (unstable angina) or elevated (non-ST-elevation myocardial infarction) serum troponin T (TnT) levels. The definition of collected risk factors and exclusion criteria are reported in the Appendix. Diabetes mellitus was defined according to the new American Diabetes Association (ADA) criteria8 and the length of the diabetic status and the treatment duration were also recorded. Of note, none among the study population had type 1 DM. Moreover, main laboratory data, including C-reactive protein and TnT serum levels, were collected (see Appendix).

Analysis of angiographic findings

Angiographic analyses included the evaluation of CAD severity and extent by the Bogaty score,9 along with Gensini10 and Sullivan11 extent scores. The evaluation of collaterals towards the culprit vessel was performed by the Rentrop score.12 Details of the angiographic analysis are reported in the Appendix.

Frequency domain-optical coherence tomography substudy

Patient population

Seventy-two patients presenting NSTE-ACS, as their first clinical manifestation of CAD, and showing obstructive atherosclerosis at angiography were extracted from the frequency domain (FD)-OCT (FD-OCT) database of the Department of Cardiovascular Medecine, Policlinico Gemelli (Rome, Italy) and from the FD-OCT Database of the Department of Cardiovascular Medicine, San Giovanni Hospital (Rome, Italy). The databases included a cohort of 634 patients (n = 183 from Policlinico Gemelli and n = 451 from San Giovanni Hospital) who underwent FD-OCT during percutaneous coronary interventions for a de novo coronary lesion between December 2009 and June 2012. The definition of risk factors and exclusion criteria are reported in the Appendix. Diabetes was defined with the ADA criteria8 similarly to the angiographic substudy.

Frequency domain-optical coherence tomography procedure and analysis of optical coherence tomography images

Frequency domain-optical coherence tomography was performed with a non-occlusive techniques as previously described.13 Frequency domain-optical coherence tomography images were acquired with a commercially available system (C7 System; LightLab Imaging Inc/St Jude Medical, Westford, MA, USA) after the OCT catheter (C7 Dragonfly; LightLab Imaging Inc/St Jude Medical, Westford, MA, USA) was advanced to the distal end of the target lesion. Frequency domain-optical coherence tomography analysis was divided into two parts: the analysis performed at the minimal lumen area (MLA) site and the analysis conducted along the culprit vessel.14 Details of the OCT analysis are reported in the Appendix.

Statistical analysis

Normal distribution was assessed by the Kolmogorov–Smirnov test. Variables which did not follow a normal distribution were expressed as median and inter-quartile range, whereas other continuous variables were expressed as means ± standard deviation; categorical variables were expressed as proportions. Continuous variables were compared by Student's t-test or Mann–Whitney U test as appropriate, whereas categorical variables by Fisher's exact test. Correlations between variables were performed using the Pearson test or the Spearman's rank test, as appropriate. A multiple logistic regression was performed to assess independent predictors of the Rentrop score having Rentrop 2–3 as a reference variable, while multiple linear regression was performed to assess independent predictors of the stenosis score and the extent index. At this scope, in the multiple regression analysis model, we included variables showing a P < 0.10 at univariate analysis. P < 0.05 was always required for statistical significance. The software SPSS 17.0 (SPSS Italia, Florence, Italy) was used for statistical analyses.

Results

Angiographic substudy

Clinical features

Overall we included 167 patients admitted because of their very first ACS (age 67 ± 14, male sex 122 patients, 73%). Nearly one-third of patients was type II diabetic (47 patients, 28%). The mean length of the diabetic status was 10 ± 8 years, while the mean hypoglycaemic treatment duration was 8 ± 5 years. Table 1 summarizes clinical, angiographic, and laboratory data in diabetic and non-diabetic patients. Diabetic patients had higher levels of LDL-cholesterol and triglycerides (109 ± 43 vs. 99 ± 31 mg/dL, P = 0.02 and 142 ± 67 vs. 117 ± 54 mg/dL, P = 0.04, respectively). C-reactive protein levels were similar in the two groups as well as the rate of statin use [5.29 mg/dL (2.13–7.23) vs. 5.64 mg/dL (2.45–8.11), P = 0.32 and 64 vs. 61%, P = 0.86, respectively]. Troponin T levels were similar between diabetic and non-diabetic patients (P = 0.41).

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Table 1

Baseline characteristics of the population undergoing angiographic study according to diabetes presence

VariablesOverall population (n = 167)Diabetic patients (n = 47)Non-diabetic patients (n = 120)P-value
Age (years)67 ± 1469 ± 1266 ± 140.11
Male sex, n (%)122(73)31(66)91(76)0.24
Risk factors
 Hypertension, n (%)119(71)35(75)84(70)0.71
 Smoking, n (%)95(57)23(49)72(60)0.22
 Dyslipidaemia, n (%)80(48)25(53)55(46)0.49
 Family history, n (%)61(36)12(25)49(41)0.08
 BMI26.9 ± 2.826.7 ± 2.825.9 ± 2.30.53
Clinical presentation
 UA, n (%)88(53)25(53)63(52)0.91
 NSTEMI, n (%)79(47)22(47)57(47)
Haemodynamic parameters
 Heart rate (b.p.m.)69 ± 2372 ± 2567 ± 210.31
 Systolic arterial pressure (mmHg)143 ± 26145 ± 25142 ± 240.68
 Dyastolic arterial pressure (mmHg)83 ± 1585 ± 1681 ± 150.61
Therapy on admission
 Aspirin, n (%)83(50)22(47)61(51)0.73
 Beta-blockers, n (%)87(52)25(53)62(52)0.86
 Statins, n (%)103(62)30(64)73(61)0.86
 ACE-I/ARB, n (%)89(53)27(57)62(52)0.60
Angiographic characteristics
 Multi-vessel disease, n (%)82(49)32(68)50(42)0.003
 Total occlusion in the entire coronary artery tree, n (%)43(26)18(38)25(21)0.03
 Total/subtotal occlusion, n (%)83(49)26(47)57(55)0.39
Laboratory data
 Red blood cells (109/L)4.69 ± 0.534.65 ± 0.624.70 ± 0.490.57
 Haemoglobin (g/dL)13.6 ± 1.713.3 ± 1.913.7 ± 1.60.17
 White blood cells (109/L)9.82 ± 3.9610.2 ± 3.249.74 ± 3.660.53
 Platelets (109/L)236 ± 78234 ± 65238 ± 830.72
 Mean platelet volume (Fl)9.69 ± 1.739.82 ± 1.789.64 ± 1.780.51
 Serum creatinine (mg/dL)1.09 ± 0.221.10 ± 0.241.07 ± 0.190.38
 eGFR (mL/min/1.73 mq)69.2 ± 14.668.4 ± 11.376.2 ± 13.50.25
 Serum glucose (mg/dL)121 ± 49121 ± 51119 ± 440.77
 Total cholesterol (mg/dL)181 ± 52184 ± 56170 ± 400.18
 LDL (mg/dL)106 ± 41109 ± 4399 ± 310.02
 HDL (mg/dL)43 ± 1342 ± 1345 ± 130.33
 Triglycerides (mg/dL)136 ± 64142 ± 67117 ± 540.04
C-reactive protein (mg/dL)5.13(2.54–8.12)5.29(2.13–7.23)5.64(2.45–8.11)0.32
 Fibrinogen (mg/dL)365 ± 131378 ± 160339 ± 1160.14
 TnT (ng/mL)2.04 ± 1.922.08 ± 1.931.98 ± 1.970.41
  • ACE-I/ARB, Angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers; BMI, body mass index; b.p.m., beats per minute; CK, creatinine-kinase; eGFR, estimated glomerular filtration rate; HDL, high-density lipoproteins; LDL, low-density lipoproteins; mmHg, millimetres of mercury; NSTEMI, non-elevation myocardial infarction; TnT, troponin T; UA, unstable angina.

Angiographic findings

The multi-vessel disease rate was higher in diabetic when compared with non-diabetic patients (73% vs. 16%, P = 0.003), as well as the total occlusion rate in non-culprit coronary branches (38 vs. 21%, P = 0.03); whereas the coronary occlusion/subocclusion rate at the target vessel was similar between diabetic and non-diabetic patients (47 vs. 55%, P = 0.39). At univariate analysis, indexes of angiographic CAD severity, according to the Bogaty score, were higher in diabetic when compared with non-diabetics patients (stenosis score 3.89 ± 1.99 vs. 2.56 ± 1.64, P = 0.001, extent index 1.34 ± 0.74 vs. 0.68 ± 0.43, P = 0.001) (Figure 2) (Tables A1 and A2 in the Appendix). The stenosis score was higher in males than in females (3.01 ± 1.89 vs. 2.44 ± 1.49, P = 0.03). The stenosis score tended to correlate with LDL levels (R: 0.17, P = 0.08). The extent index was lower in patients on statins than in those not taking statins (0.71 ± 0.55 vs. 0.95 ± 0.63, P = 0.01). Extent index correlated with age (R: 0.27, P = 0.001), and tended to correlate with LDL levels (R: 0.29, P = 0.08). At multivariate analysis (Table 2), both the stenosis score and the extent index were associated with diabetes (P = 0.001). The stenosis score was associated also with male sex (P = 0.03) and the extent index also with the lack of statin treatment (P = 0.05).

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Table 2

Multiple regression analysis for stenosis score, extent index, Gensini score, and Sullivan score

VariablesBESP-value
Stenosis score
 Gender (male)0.620.290.03
 Diabetes mellitus0.950.290.001
Extent index
 Age0.010.050.07
 Diabetes mellitus0.490.100.001
 Statins−0.170.080.05
 LDL0.050.030.17
Gensini score
 Age0.160.100.07
 Diabetes mellitus0.420.160.02
Sullivan score
 Gender (male)0.540.230.06
 Diabetes mellitus0.610.390.05
 LDL0.190.210.09
  • LDL, low-density lipoproteins.

Figure 2

Extent index and age according to the presence or absence of diabetes mellitus. Diabetic patients had a significantly higher extent index when compared with patients without diabetes mellitus (P = 0.001), while age did not differ between the two groups.

Moreover, at univariate analysis, Gensini and Sullivan scores were higher in diabetic [18 (4–68) and 21 ± 14] when compared with non-diabetic patients [11 (2–46) and 15 ± 12] (P = 0.002 and P = 0.05, respectively). At multivariate analysis (Table 2), both the Gensini score and the Sullivan score were associated with diabetes (P = 0.02 and P = 0.05, respectively).

Good collaterals were more frequent in diabetic than in non-diabetic patients (68 vs. 13%, P = 0.001, Figure 3) and in patients with single-vessel disease than in patients with multi-vessel disease (91 vs. 71%, P = 0.05). Patients with good when compared with those with poor collaterals had a similar stenosis score (3.12 ± 1.77 vs. 3.85 ± 2.03, P = 0.09) and a lower extent index (0.78 ± 0.54 vs. 1.16 ± 0.75, P = 0.05). Patients with good when compared with those with poor collateral tended to have lower levels of TnT on admission (1.89 ± 1.94 vs. 2.61 ± 2.27, P = 0.08). At multivariate analysis, good collaterals were associated with diabetes (OR: 2.33, 95% CI: 1.45–6.54, P = 0.002) and inversely with the extent index (OR: 0.87, 95% CI: 0.51–0.96, P = 0.03) and with the stenosis score with borderline significance (OR: 0.91, 95% CI: 0.78–0.99, P = 0.06). Of note, non-diabetic patients with good collaterals had a lower extent index than those with poor collaterals, whereas diabetics patients with good or poor collaterals had a similar extent index (0.65 ± 0.41 vs. 0.98 ± 0.57, P = 0.03 for non-diabetics and 1.41 ± 0.75 vs. 1.33 ± 0.74, P = 0.67 for diabetic patients, P for interaction 0.04, Figure 4).

Figure 3

The rate of Rentrop 0–1 and Rentrop 2–3 in patients with and without diabetes mellitus. Good collaterals were more frequent in diabetics than in non-diabetic patients (P = 0.001).

Figure 4

The extent index in patients with and without diabetes mellitus stratifying for Rentrop classification (Rentrop 0–1 vs. Rentrop 2–3). Non-diabetic patients had a lower extent index when stratifying for angiographic quality of collaterals, whereas diabetics patients had a similar extent index when stratifying for the angiographic quality of collaterals (P = 0.03 and P = 0.67, respectively, P for interaction 0.04).

Optical coherence tomography substudy

Clinical features

Overall, we included 72 patients (age 65 ± 9, male sex 55 patients, 76%) (n = 26 from Policlinico Gemelli and n = 46 from San Giovanni Hospital). Twenty-nine patients (40%) were diabetic while 43 (60%) were non-diabetic. The mean length of diabetes was 9 ± 6 years, while the mean hypoglycaemic treatment duration was 7 ± 4 years. Table 3 summarizes clinical and angiographic findings between diabetic and non-diabetic. The multi-vessel disease rate was higher in diabetic when compared with non-diabetic patients (69 vs. 42%, P = 0.03).

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Table 3

Baseline characteristics of cohort of patients undergoing frequency domain-optical coherence tomography according to diabetes presence

VariablesOverall population (n = 72)Diabetic patients (n = 29)Non diabetic patients (n = 43)P-value
Age (years)65 ± 964 ± 766 ± 90.42
Male sex, n (%)55(76)22(76)36(84)0.54
Risk factors
 Hypertension, n (%)50(69)19(65)31(72)0.60
 Smoking, n (%)45(62)17(59)28(65)0.62
 Dyslipidaemia, n (%)38(53)19(65)19(44)0.12
 Family history, n (%)29(40)9(31)20(46)0.22
 BMI27.2 ± 1.526.8 ± 1.727.3 ± 1.40.52
Clinical presentation
 UA, n (%)38(53)15(52)23(53)1
 NSTEMI, n (%)34(47)14(48)20(47)
Haemodynamic parameters
 Heart rate (b.p.m.)61 ± 2161 ± 1963 ± 230.73
 Systolic arterial pressure (mmHg)149 ± 24150 ± 24147 ± 250.54
 Dyastolic arterial pressure (mmHg)88 ± 1488 ± 1585 ± 150.34
Therapy on admission
 Aspirin, n (%)33(46)14(48)19(44.2)0.81
 Beta blockers, n (%)37(51)16(55)16(37)0.15
 Statins, n (%)33(46)16(55)17(39)0.23
 ACE-I/ARB, n (%)37(51)18(62)19(44)0.15
Angiographic characteristics
 Multi-vessel disease, n (%)38(53)20(69)18(42)0.03
  • ACE-I/ARB, angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers; b.p.m., beats per minute; BMI, body mass index; mmHg, millimetres of mercury; NSTEMI, non-elevation myocardial infarction; UA, unstable angina.

Frequency domain-optical coherence tomography findings

The FD-OCT analysis performed at the MLA site (Table 4 and Figure 5) showed less lipid quadrants and a smaller lipid arc in diabetic than in non-diabetic patients (2.3 ± 1.3 vs. 3.0 ± 1.2, P = 0.03 and 198° ± 121° vs. 260° ± 118°, P = 0.03), while we failed to identify significant differences with regard to plaque type, fibrous cap thickness, thin-cap fibroatheroma, and MLA between the two groups.

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Table 4

Frequency domain-optical coherence tomography analysis performed at the minimum lumen area site and frequency domain-optical coherence tomography findings along the culprit vessel

VariablesDiabetic patients (n = 29)Non-diabetic patients (n = 43)P-value
Analysis performed at the MLA site
 Lipid plaque, n (%)22 (76)38 (88)0.16
 Fibrous plaque, n (%)3 (10)4 (9)
 Calcified plaque, n (%)4 (14)1 (2)
 Fibrous cap thickness, µm64±3864±370.95
 TCFA, n (%)12 (41)19 (44)1
 MLA, mm22.1±1.02.2±1.20.80
 Lipid quadrants, n2.3±1.33.0±1.20.03
 Lipid arc, degrees198±121260±1180.03
Findings along the culprit vessel
 Plaque fissure, n (%)15 (39)17 (52)0.34
 Plaque erosion, n (%)1 (3)2 (5)1
 Thrombus, n (%)11 (38)19 (44)0.63
 Minimal fibrous cap thickness, µm62±3460±220.82
 Microvessel presence, n (%)26 (90)33 (77)0.21
 Calcified nodule, n (%)23 (79)23 (54)0.04
 Normal vessel, %40.7±22.834.2±28.70.30
 Calcified vessel, %21.9±16.117.7±21.80.37
 Lipid vessel, %41.1±25.354.4±30.10.04
 Maximal calcium quadrantsa, n1.7±1.01.2±0.90.03
 Maximal calcium arca, °126±9581±800.03
 Maximal lipid quadrantsa, n2.7±0.73.0±0.80.30
 Maximal lipid arca, degrees219±85248±940.18
  • MLA, minimal lumen area; TCFA, thin-cap fibroatheroma.

  • aIt refers to the cross-section with the highest quadrant number and the greatest area of calcium or lipids.

Figure 5

Optical coherence tomography findings at the minimal lumen area (MLA) site (A and B) and along the culprit vessel (CE) in diabetic and non-diabetic patients. (A) Lipid plaque with a lipid arc of 121° was imaged at the MLA site in a diabetic patient. (B) Lipid plaque with a wider lipid arc (360°) was imaged at the MLA site in a non-diabetic patients. (C) Calcified plaque with a large calcified arc (161°) was imaged along the culprit vessel in a diabetic patient. (D) A typical superficial calcified nodule in a diabetic patient. The nodule has a length of 1.87 mm and a mean depth of 0.68 mm. (E) Calcified plaque with a small calcified arc (89°) was imaged along the culprit vessel in a non-diabetic patients.

The FD-OCT analysis conducted along the culprit vessel (Table 4 and Figure 5) showed that the most calcified cross-section had a greater number of calcified quadrants and a larger calcified arc in diabetic patients than in non-diabetic patients (1.7 ± 1.0 vs. 1.2 ± 0.9, P = 0.03 and 126° ± 95° vs. 81° ± 80°, P = 0.03). The rate of superficial calcified nodules was significantly higher in diabetic than in non-diabetic patients (79 vs. 54%, P = 0.04). Furthermore, a lower percentage of vessel with a lipid plaque was observed in diabetic vs. non-diabetic patients (41.1 ± 25.3 vs. 54.4 ± 30.1%, P = 0.04). We failed to find differences with regard to rates of plaque rupture, erosion, thrombus and microvessel presence between the two groups. No significant difference was found when comparing diabetic vs. non-diabetic patients with regard to the rate of calcified vessel. The cross-sections with the highest lipid quadrant number and the greatest lipid arc were similar between the two groups.

Discussion

In our angiographic substudy, we demonstrate that at the time of their first ACS, type II diabetic patients have more severe and extensive coronary atherosclerosis when compared with non-diabetic patients. As pro-thrombotic stimuli are stronger in diabetic than non-diabetic patients, it follows that the former may have protective factors that prevent a first ACS in spite of more severe atherosclerosis. We also found that type II diabetic patients had better collateral circulation towards the culprit vessel than non-diabetic patients, in spite of a greater atherosclerotic burden in the former. Interestingly, both DM and a lower extent index independently predicted good collateral development. This apparent contradiction is probably accounted for by the fact that DM offsets the detrimental effects of a high extent index on collateral development. Accordingly in a subset analysis non-diabetic patients with good collaterals had a lower extent index than those with poor collaterals, whereas diabetic patients with good or poor collaterals had a similar extent index. Furthermore, it is worth noting, as reported in the OCT substudy, that MLA at the site of the culprit stenosis was similar between diabetic and non-diabetic patients. Thus, differences in the severity of culprit stenosis are unlikely to explain the better collateral development observed in diabetic vs. non-diabetic patients. Taken together these observations suggest that enhanced collateral circulation may help protecting diabetic patients from a first acute event when compared with non-diabetic patients.

The optical coherence tomography assessment of the culprit vessels confirmed that coronary atherosclerosis had different features in diabetic when compared with non-diabetic patients, as the former exhibited more calcium and less lipid content in the culprit vessel, which might make the vessel less thrombogenic.

This study has some limitations. A first limitation is the angiographic evaluation of CAD severity and extent by angiography rather than by intravascular ultrasound (IVUS). Moreover, we have not utilized functional tests that may have provided better and more quantitative measurements of collateral development. However, a good correlation between invasive measures of collateral function and the Rentrop score has been reported both in case of acute and coronary chronic occlusions.15,16 A further limitation is the lack of calcium score evaluation by multisliced computed tomography and of lipid plaque assessment by new imaging modalities such as near-infrared spectroscopy. Optical coherence tomography is emerging as a powerful technique in order to characterize plaque composition, although it has intrinic limitations in penetration depth and does not allow chemical composition identification which is instead possible by near-infrared spectroscopy. Thus, a multi-modality imaging approach able to fully characterize coronary atherosclerosis composition (high resolution probes with tissue characterization capabilities, deep penetration, and possibly molecular imaging)17 is the way forward. Another limitation is that we do not provide any mechanistic interpretation of our finding in addition to those suggested by the assessment of collateral circulation and by OCT analysis. A last limitation is the small number of patients, in particular, in the OCT substudy. We hope that our novel and intriguing observations will open the way to new studies based on multi-modality imaging techniques and focusing on mechanisms in larger patient populations. Finally, as our study is focused on type II diabetic patients, our findings should be confirmed in type 1 diabetic patients also.

Severity and features of coronary atherosclerosis in diabetics

The association of DM with a more extensive CAD has been reported in post-mortem,4 angiographic,2 and IVUS-based studies3 as well as, more recently, in studies based on multislice coronary computed angiography.18

Ledru et al.2 found more severe and extensive CAD in diabetic than in non-diabetic patients by angiography and a correlation between fasting plasma glucose levels and CAD severity. Accordingly, Nicholls et al.3 in 654 patients enrolled in the REVERSAL study showed by using IVUS that diabetes was an independent predictor of an increased atherosclerotic burden. Furthermore, Mintz et al.19 in 884 patients undergoing IVUS examination of angiographically normal coronary reference segments showed that the presence of an atheroma burden was associated with diabetes. Interestingly, Hambly et al.20 showed that diabetic patients exhibited a higher angiographic coronary stenosis score than non-diabetic patients only for triglycerides levels above the median of the population, suggesting an interaction between diabetes and triglycerides in promoting CAD.

Taken together, these observations indicate that DM is a strong pro-atherogenic stimulus, as also suggested by the frequent involvement of other vascular districts in diabetic when compared with non-diabetic patients.21 Of note, CAD severity and extent are predicted by AGEs levels and oxidative stress,22 but not by C-reactive protein levels.23 Although, the clustering with other risk factors has been advocated in the past as a potential mechanism of the association of DM with CAD,24 most studies showed an independent association.1 Accordingly, the predictive value of DM for the extent index in our study was independent of LDL levels and of statin use.

Our OCT substudy shows that diabetic patients have more calcium burden than non-diabetic patients in the culprit vessel. In a study, based on radio-frequency IVUS, Nasu et al.,25 in agreement with our findings, found that the percentage area of dense calcium was significantly larger in the explored segment of diabetic patients when compared with non-diabetic patients and that the rate of virtual histology-derived fibrocalcific atheroma was higher in diabetics vs. non-diabetics. Moreover, a higher calcium score was demonstrated by multisliced computed tomography in diabetic patients when compared with non-diabetic patients, and in type II diabetes when compared with type 1 diabetes.26,27 Finally higher rates of calcification and dissection were detected by OCT in diabetic than in non-diabetic patients with unstable angina in a recent study by Feng et al.28 Interestingly, we found that superficial calcified nodules were more frequently in diabetic than in non-diabetic patients. Calcified nodules are considered by pathologist as a less common form of vulnerable plaque.29 Thus, our study suggests that this pathological substrate of instability might be more frequent in diabetic patients.

Our OCT substudy shows also that lipidic plaque composition is less represented in type II diabetic patients both at the MLA site and along the explored vessel. However, in a previous post-mortem study, Burke et al.4 showed that, among patients dying of sudden cardiac death, diabetic patients had a larger necrotic core than non-diabetic patients and two virtual histology-IVUS studies25,30 showed a higher lipid content in diabetic patients both at the level of the culprit lesion and along the explored segment. Conversely, the rate of yellow plaque by angioscopy in patients with unstable angina was similar between diabetics and non-diabetics in a study by Silva et al.,31 while two recent OCT studies failed to show differences in a lipidic plaque content between diabetic and non-diabetic patients.28,32 Our study, expands these results by exploring the culprit vessel segment in addition to the MLA site in a well-characterized population of patients.

Our findings may differ from previous studies for several reasons, including different imaging modalities, different study populations, and different lengths of the explored segments. It is worth nothing that our study is the first to compare plaque composition by OCT in diabetic and non-diabetic patients at the time of a very first acute coronary event.

Taken together, in vivo studies by intravascular imaging suggest that diabetic patients have a peculiar coronary atherosclerotic pattern, which may be characterized by a predominance of calcium over a lipid burden in the culprit vessel. Thus, in type II diabetic patients the plaque responsible for a first ACS might be less thrombogenic than that in non-diabetic patients; this might help explaining why the first ACS occurs in the former when CAD is more severe.

Coronary collaterals and diabetes

We found more developed collateral circulation towards the culprit vessel in type II diabetic than in non-diabetic patients. Previous angiographic studies provided contrasting results.3339 In an angiographic study, Abaci et al.33 found less collaterals in diabetic than in non-diabetic patients, while Nathoe et al.34 failed to show an association between diabetes and collateral development among 561 patients enrolled in the OCTOPUS study. Similarly, Fujita et al.35 failed to show a similar association among 248 patients presenting with an acute myocardial infarction. However, the designs of our and of previous studies are different.

Studies performed with an invasive assessment of collateral circulation have provided, again, conflicting results. Indeed, Werner et al.36 showed that distal to a chronic total occlusion collateral pressure index was similar in diabetic and non-diabetic patients. Yet, in another study, an acute recruitment of collaterals of a recanalized coronary occlusion after balloon reocclusion was impaired in diabetic patients.37 In contrast, Kyriakides et al.38 showed similar collateral recruitment between diabetic and non-diabetic patients using back pressure-derived indexes. Similar findings were observed in another study by de Vries et al.39 by using pressure-derived indexes in stable patients scheduled for percutaneous coronary intervention. Discrepancies, among studies, may be due to different patient populations and different ways of collateral evaluation.40

Taking together, angiographic and functional studies have so far produced discordant findings. Our study in an highly selected population of patients with a very first acute coronary event shows by angiography that collaterals towards the culprit vessel are better developed in diabetic than in non-diabetic patients. Owing to the more robust information provided by an invasive assessment of the collateral circulation, our results need to be confirmed by future studies with the functional evaluation of the collateral circulation.

The mechanisms underlying the association of diabetes with collateral development in our study cannot be deduced by our results. It is worth noting that our findings are in keeping with a recent observation by Lin et al.41 who showed an interaction between diabetes and the VEGF +405>G polymorphism which was associated to a better collateral development.

Protective factors against a first acute coronary event in diabetic patients

In the early 1900s, Bogaty et al.9 showed that diabetes was more frequent among patients presenting initially with stable angina when compared with those presenting with an unheralded myocardial infarction. Furthermore, Gaspardone et al.42 compared risk factors in patients presenting with unheralded myocardial infarction and one-vessel disease and patients presenting with stable angina and multi-vessel disease; they found that the prevalence of diabetes was strikingly higher in the latter. This is rather intriguing as diabetic patients should theoretically suffer a first acute event for a lesser severity of CAD because of their intense pro-thrombotic, pro-inflammatory, and pro-oxidant state.7 Thus, it would appear that yet unknown protective mechanisms might delay the onset of a first acute event in diabetic patients.

Our study confirms these previous observations and suggests that both some features of CAD and enhanced collateral development may protect diabetic patients from a first ACS. The observation of more calcific plaques with less lipid deposition, less prone to cause coronary thrombosis, is in line with the observation that diabetic patients when compared with non-diabetic patients exhibit higher circulating levels of pro-calcifying progenitor cells.43 Furthermore, in this study we found that angiographically visible collaterals towards the culprit vessel are better developed in diabetic than in non-diabetic patients despite a similar stenosis severity.

Further studies will probably identify other, and possibly more important, causes of the paradox that in diabetic patients the first acute event occurs in the presence of more severe CAD. Interestingly, several epidemiological studies have demonstrated a lower prevalence of abdominal aortic aneurysms in diabetic than in non-diabetic patients.44 Accordingly, a recent study in experimental models of abdominal aneurysm has showed that hyperglycaemia reduces aneurysm progression and this protective effect is mediated by inhibition of matrix metalloproteinase-9, thus reducing elastin degradation.45 Similar protective mechanisms might well operate in the coronary circulation.

Conflict of interest: none declared.

Appendix

Angiographic substudy

Definition of risk factors

In all patients cardiovascular risk factors were carefully examined, including a family history of early CAD (first degree relative with a history of myocardial infarction < 60 years), hypercholesterolaemia (total cholesterol >200 mg/dL or treated hypercholesterolaemia), smoking [current regular use (any amount) or cigarette withdrawal 2 months], and hypertension (systolic blood pressure >140 mmHg and/or diastolic blood pressure >90 mmHg or treated hypertension). The body mass index also was obtained.

Laboratory assessment

Total blood cells count (ADVIA, Bayer Diagnostic), estimated the creatinine-derived glomerular filtration rate, assessed by Cockcroft–Gault formula, glycaemia, lipid profile (total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides), and fibrinogen. C-reactive protein was measured using a high-sensitivity latex-enhanced immunonephelometric assay (Latex/BN II, Dade Behring, Marburg, Germany). Troponin T was measured by the Troponin T Elecsys assay (Roche Diagnostics, Mannheim, Germany).

Exclusion criteria

Exclusion criteria were: previous ischaemic heart disease [n = 963 patients: stable angina (n = 479 patients); NSTE-ACS (n = 289 patients); ST-elevation myocardial infarction (n = 93); silent ischaemia requiring myocardial revascularization (n = 102 patients)], severe chronic heart failure (NYHA class III/IV; n = 52 patients), severe valvular disease (n = 43 patients), systemic inflammatory diseases as acute and chronic infections (n = 36 patients), autoimmune diseases (n = 13 patients), renal or liver failure (n = 37 patients), neoplasia (n = 18 patients), evidence of immunologic disorders (n = 42 patients), use of anti-inflammatory or immunosuppressive drugs (n = 49 patients), and recent (3 months) surgical procedures or trauma (n = 36 patients).

Frequency domain-optical coherence tomography substudy

Definition of risk factors

Risk factors were defined as reported above.

Exclusion criteria

Exclusion criteria were similar for that reported in the angiographic substudy along with other lesion-related exclusion criteria specific for the OCT substudy including ostial lesions of the left main and right coronary artery, or OCT assessment only after stent deployment (no pre-percutaneous coronary intervention OCT pullback). Two hundred and sixty-two patients out of 451 patients were excluded by clinical criteria (58%), while 117 were excluded by lesion-related exclusion criteria (25%).

Analysis of angiographic findings

Two expert angiographers (A.L. and S.G.), who were blinded to the clinical and laboratory values, evaluated all angiographic images to assess severity and the extent of CAD.9 Any disagreement between the two angiographers was resolved by consensus; when consensus could not be reached, a third experienced angiographer (N.C.) assessed and classified the parameters under evaluation. All analyses were done after intracoronary nitrates injection.

Severity of the disease was assessed by three different parameters: (i) number of diseased vessels, including the number of major epicardial vessels with ≥70% narrowing of the lumen diameter. The maximum number of vessel diseased was three and patients with at least two major vessels involved were classified as multi-vessel disease; (ii) the stenosis score refers to the total number of >50% narrowings in all vessels of the angiogram. A maximum of three stenosis was permitted per coronary arterial segment; (iii) number of occlusions (TIMI 0-1).

The extent of disease (extent index) was obtained by dividing the extent score of the entire coronary arterial tree by the number of analysed segments. A segment was scored 0 if it appeared angiographically normal, 1 if <10% of its length appeared abnormal (narrowed and/or irregular), 2 if >10 up to 50% of its length was abnormal, and 3 if >50% of its length was abnormal. Since 15 segments were considered, the extent index could vary from 0 to 3. Moreover, additional quantitative angiographic scores, using the Gensini10 and Sullivan11 extent systems, were calculated. The Gensini score quantifies severity of CAD by a non-linear points system for the degree of luminal narrowing along with a multiplier for specific coronary tree locations, thereby weighting each lesion score for prognostic significance. The total of the lesion scores is summed to give a final Gensini score. Thus, multiple severe proximal lesions gain the highest score. The Sullivan extent score quantifies the percentage of the coronary intimal surface area affected by atheroma, without specific weighting for the degree of luminal narrowing. The percentage involvement of each vessel is estimated and multiplied by a factor representative of the surface area of that vessel in relation to the entire coronary tree. We used a modified version based on segments of each vessel with reported disease to derive percentage involvement. Four segments of the right coronary artery each contributing 25%; three segments of the left anterior descending artery each contributing 33% with the proximal segment further subdivided into two; the left circumflex artery divided into three segments each contributing 33%.

Finally, in patients presenting with total or subtotal occlusion of the culprit coronary branch (TIMI 0-2), collateral filling was graded according to the Rentrop classification12: 0, no filling of any collaterals; 1, filling of side branches of the coronary artery by collaterals without visualization of the epicardial segment; 2, partial filling of the epicardial coronary artery by collaterals; 3, complete filling of the epicardial coronary artery by collaterals. This was obtained for collaterals towards the target vessel only. Patients were dichotomized according to angiographic signs of collaterals [Rentrop 0–1 (poor collaterals) vs. Rentrop 2–3 (good collaterals)].

Frequency domain-optical coherence tomography procedure and analysis of optical coherence tomography images

Frequency domain-optical coherence tomography was performed with a 6 Fr guiding catheter in all patients. Unfractionated heparin was administrated during FD-OCT, with a target activated clotting time of >250 s. A 0.014-inch guidewire was placed distally in the target vessel and an intracoronary injection of 200 µg of nitroglycerin was performed. Frequency domain-optical coherence tomography images were acquired with a commercially available system (C7 System; LightLab Imaging, Inc.,/St Jude Medical, Westford, MA, USA) after the OCT catheter [C7 Dragonfly; LightLab Imaging, Inc.,/St Jude Medical, Westford, MA, USA with an imaging tip of 2.7 Fr (diameter = 0.9 mm) resulting in an catheter area of 0.63 mm2] was advanced to the distal end of the target lesion. The FD-OCT run was conducted before either direct stent implantation or balloon pre-dilatation (n = 5 patients, with a small 1.5 mm balloon inflated at 8 atmosphere, in order to allow OCT evaluation in case of occlusive catheter) using the integrated automated pullback device at 20 mm/s. This was felt necessary in order to achieve an optimal image of the superficial vessel components. In fact, stent struts are characterized by a shadowing effect that may impair the analysis of the underlying structures. None of the enrolled patients were treated with thrombectomy before OCT. During image acquisition, the coronary blood flow was replaced by continuous flushing of contrast media directly from the guiding catheter at a rate of 4 mL/s with a power injector (Medrad Avanta, Siemens, Germany) in order to create a virtually blood-free environment.

All images were recorded digitally, stored, and analysed every single frame (0.2 mm) by two independent investigators (F.P. and L.D.V.), who were blinded to clinical and laboratory data. Offline analysis was performed with proprietary software (LightLab Imaging) after confirming proper calibration settings of the Z-offset.

Frequency domain-optical coherence tomography analysis12 was divided into two parts: the analysis performed at the MLA site and the analysis conducted along the culprit vessel.

At the MLA site the analysis was targeted on plaque characterization, the presence of thin-cap fibroatheroma and the lumen area. When a plaque contained two or more lipid-containing quadrants, it was considered a lipid-rich plaque, and the lipid arc and the cap thickness were measured. Thin-cap fibroatheroma was defined as a lipid-rich plaque with a fibrous cap thickness of ≤65 µm.

The FD-OCT analysis performed along the culprit vessel was targeted to detect plaque fissure, plaque erosion, thrombus, the presence of superficial calcified nodules, and the presence of micro-vessels. The presence of plaque fissure was identified by the presence of fibrous cap discontinuity/disruption and cavity formation in the context of the plaque which communicates with the lumen, while plaque erosion by the presence of intracoronary thrombus adjacent to the luminal surface of the plaque in the absence of detectable signs of overlying fibrous cap rupture.

The percentage of normal vessel was assessed as the length (mm) of the vessel without any type of FD-OCT defined plaque divided by the length of the entire imaged vessel (mm) and multiplied by 100. The percentage of calcified vessel was assessed as the sum of the calcified plaque longitudinal lengths (mm) along the imaged vessel divided by the length of the entire imaged vessel (mm) and multiplied by 100. The percentage of lipid vessel was assessed as the sum of the lipid plaque longitudinal lengths (mm) along the imaged vessel divided by the length of the entire imaged vessel (mm) and multiplied by 100. The cross-section that had the highest quadrant number and the greatest arc of calcium or lipids was recorded together with the minimal fibrous cap thickness.

Dichotomous variablesStenosis scoreP-value
Diabetes
 Yes3.89 ± 1.990.001
 No2.56 ± 1.64
Gender
 Male3.01 ± 1.890.03
 Female2.44 ± 1.49
Family history
 Yes2.99 ± 1.820.20
 No2.62 ± 1.73
Clinical presentation
 UA2.94 ± 1.820.28
 NSTEMI2.65 ± 1.77
Smoking
 Yes2.97 ± 1.950.34
 No2.70 ± 1.61
Dyslipidaemia
 Yes2.99 ± 1.970.37
 No2.73 ± 1.63
Beta-blockers
 Yes2.93 ± 1.840.58
 No2.77 ± 1.78
ACE-I/ARB
 Yes2.80 ± 1.810.66
 No2.92 ± 1.82
Statins
 Yes2.78 ± 1.720.67
 No2.90 ± 1.86
Aspirin
 Yes2.90 ± 1.830.73
 No2.81 ± 1.80
Hypertension
 Yes2.79 ± 1.450.74
 No2.88 ± 1.93
Continuous variablesRP-value
 LDL0.170.08
 C-reactive protein0.150.11
 Age0.130.14
 CK0.110.14
 Mean platelet volume−0.100.16
 Length of diabetic statusa0.110.18
 Hypoglycaemic treatment durationa0.100.19
 Serum glucose0.090.21
 Fibrinogen0.080.23
 CK-MB0.090.24
 Serum creatinine0.060.40
 Total cholesterol0.060.41
 GFR0.120.43
 Red blood cells0.050.46
 Haemoglobin−0.050.51
 BMI0.320.56
 Triglycerides0.050.59
 TnT0.020.71
 White blood cells−0.010.81
 HDL−0.220.82
 Platelets0.010.85
  • ACE-I/ARB, angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers; BMI, body mass index; CK, creatinine-kinase; GFR, glomerular filtration rate; HDL, high-density lipoproteins; LDL, low-density lipoproteins; NSTEMI, non-elevation myocardial infarction; TnT, troponin T; UA, unstable angina.

  • aIn diabetic patients only.

  • Table A1

    Correlation between main dichotomous and continuous variables and stenosis score

    Dichotomous variablesExtent indexP-value
    Diabetes
     Yes1.34 ± 0.740.001
     No0.68 ± 0.43
    Statins
     Yes0.71 ± 0.550.01
     No0.95 ± 0.63
    Family history
     Yes0.77 ± 0.490.11
     No0.91 ± 0.66
    Beta-blockers
     Yes0.79 ± 0.550.17
     No0.93 ± 0.65
    Gender
     Male0.83 ± 0.590.20
     Female0.97 ± 0.64
    Smoking
     Yes0.83 ± 0.590.41
     No0.91 ± 0.64
    Clinical presentation
     UA0.94 ± 0.620.43
     NSTEMI0.85 ± 0.57
    Occlusion
     Yes0.79 ± 0.590.46
     No0.87 ± 0.61
    Aspirin
     Yes0.89 ± 0.610.52
     No0.84 ± 0.61
    ACE-I/ARB, n (%)
     Yes0.84 ± 0.590.67
     No0.88 ± 0.62
    Dyslipidaemia
     Yes0.84 ± 0.610.69
     No0.88 ± 0.60
    Hypertension
     Yes0.85 ± 0.740.90
     No0.87 ± 0.55
    Continuous variablesRP-value
     Age0.270.001
     LDL0.290.08
     Total cholesterol0.420.14
     CK-MB0.140.17
     Mean platelet volume0.230.18
     Length of diabetic statusa0.160.21
     Hypoglycaemic treatment durationa0.150.19
     CK0.210.30
     BMI0.130.32
     Serum glucose0.120.35
     Serum creatinine0.120.38
     Platelets0.060.41
     Triglycerides0.240.41
     C-reactive protein0.050.52
     White blood cells0.110.52
     GFR0.090.56
     HDL−0.140.59
     Haemoglobin0.040.61
     Red blood cells0.030.70
     TnT0.020.81
     Fibrinogen0.030.92
  • ACE-I/ARB, angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers; BMI, body mass index; CK, creatinine-kinase; GFR, glomerular filtration rate; HDL, high-density lipoproteins; LDL, low-density lipoproteins; NSTEMI, non-elevation myocardial infarction; TnT, troponin T; UA, unstable angina.

  • aIn diabetic patients only.

  • Table A2

    Correlation between main dichotomous and continuous variables and extent index

    References

    View Abstract