Abstract

Aims

Previous studies have reported that insulin resistance plays an important role in the progression of atherosclerosis. However, the relationship between insulin resistance and coronary plaque instability is not well established. The purpose of this study was to assess the relationship between insulin resistance and coronary plaque characteristics identified by optical coherence tomography (OCT).

Methods and results

This study enrolled 155 consecutive patients undergoing percutaneous coronary intervention. OCT image acquisitions were performed in the culprit lesions. Insulin resistance was identified using the homeostasis model assessment of insulin resistance (HOMA-IR). Subjects were divided into three tertiles according to the HOMA-IR values. Patients in the higher HOMA tertile had more frequent prevalence of lipid-rich plaques than those in the middle and lower tertiles (83 vs. 62 vs. 57%; P = 0.01). The thin-cap fibroatheroma (TCFA) prevalence rates among the higher (>2.5), middle (1.4–2.5), and lower HOMA-IR (<1.4) tertiles were 50, 29, and 26% (P = 0.02). The microvessel prevalence rates of the three tertiles were 54, 39, and 28% (P = 0.02). Furthermore, in the higher HOMA-IR group, the fibrous cap was significantly thinner compared with the other two tertiles (vs. lower HOMA-IR, P = 0.009; vs. middle HOMA-IR, P = 0.008). On multivariate analysis, acute coronary syndrome [odds ratio (OR): 17.98; 95% confidence interval (CI): 7.12−52.02; P < 0.0001] and HOMA-IR >2.50 (OR: 3.57; 95% CI: 1.42−9.55; P = 0.007) were independent predictors for the presence of TCFA.

Conclusion

This study suggests that insulin resistance might be associated with coronary plaque vulnerability.

Introduction

Diabetes mellitus (DM) is a well-known risk factor for adverse cardiovascular events and an independent poor prognostic predictor in patients with coronary artery disease (CAD).1–3 Several studies demonstrated that diabetic patients exhibit coronary plaque progression and increased coronary plaque vulnerability.4–7 In the process of promoting atherogenesis, hyperinsulinaemia resulting from insulin resistance plays an important role. Previous studies have suggested that insulin has direct atherogenic effects such as enhancement of vascular smooth muscle cell proliferation,8 and several epidemiological studies demonstrated that insulin resistance was associated with an increased risk of CAD in various ethnicities.9–11 However, few studies have examined the relationship between the degree of insulin resistance and coronary plaque vulnerability. In a previous analysis using conventional intravascular ultrasound (IVUS) and integrated backscatter IVUS (IB-IVUS), insulin resistance and hyperinsulinaemia were significantly associated with an increased lipid-rich plaque content.12,13 Although IVUS is a widely used invasive imaging technique for evaluating the coronary plaque features, optical coherence tomography (OCT) is a recent newly developed modality with a high resolution (10 µm). OCT can detect coronary plaque morphology, including microthrombi, ruptured plaques, and vulnerable plaques represented by thin-cap fibroatheroma (TCFA) more accurately than IVUS.

Homeostasis model assessment of insulin resistance (HOMA-IR) has been commonly used as a surrogate marker of insulin resistance in daily clinical practice because this simple index could evaluate insulin resistance easily and this index correlates quite well with the glucose clamp technique, recognized as the gold standard method for the estimation of insulin resistance.14 There is no report on the association between insulin resistance and coronary plaque characteristics identified by OCT. In this study, we aimed to examine the relationship between insulin resistance as measured by the HOMA-IR and coronary plaque vulnerability assessed by OCT.

Methods

Study population

From August 2008 to October 2012, 169 consecutive patients with CAD [including acute coronary syndrome (ACS) and stable angina pectoris (SAP)] who underwent percutaneous coronary intervention (PCI) for de novo culprit coronary lesions with OCT guidance and with a blood sample for glucometabolic parameter measurement at the Osaka City University were enrolled in the present study. The exclusion criteria included patients with congestive heart failure, cardiogenic shock, serum creatinine level >2 mg/dL, an intercurrent infection or other inflammatory disease, left main coronary artery lesions, total occlusions, fast plasma glucose level >140 mg/dL, patients with insulin therapy, and poor OCT image quality for analysis. Finally, according to these exclusion criteria, we enrolled 155 consecutive patients in this study. This study was approved by the hospital ethics committee, and all subjects provided informed consent before participation.

Definition of clinical diagnosis

In the present study, SAP was defined as no change in frequency, duration, or intensity of chest symptoms in the 4 weeks before intervention. ACS included unstable angina pectoris and myocardial infarction (MI). Unstable angina pectoris was defined as a progressive crescendo pattern or angina at rest without an increase in cardiac enzyme levels. MI was defined as the presence of continuous typical chest symptoms for >30 min, diagnostic electrocardiographic changes of ST-segment elevation or depression, cardiac enzyme levels of more than twice the upper limit of the normal range, and local cardiac wall motion asynergy on echocardiography. DM was identified by a diabetic clinical history or fasting plasma glucose level ≥126 mg/dL, 2 h post-load glucose level ≥200 mg/dL, glycosylated haemoglobin (HbA1c) level ≥6.5%, and/or the use of hypoglycaemic agents. Insulin resistance was estimated by the HOMA-IR values, calculated as [fasting insulin (μU/mL) × fasting plasma glucose (mg/dL)/405]. Various serum markers were measured by commercial radioimmunoassay kits and specific immunoradiometric assays. Blood samples for the assessment of glucometabolic and lipid parameters [total cholesterol, triglyceride, high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol levels] were collected from the patients after a 12 h overnight fast.

Angiographic analysis

All patients received oral aspirin (100 mg/day) and clopidgrel (300 mg loading dosage, 75 mg/day) or ticlopidine (200 mg/day) before the procedure. A 5-Fr or 6-Fr guiding catheter was used to selectively cannulate the ostium of target coronary artery through the femoral or radial artery. All captured angiographic images were analysed with offline quantitative coronary analysis (QAngio XA 7.2; Medis Medical Imaging Systems, Leiden, The Netherlands) by two experienced observers who were unaware of clinical information and results of the OCT analysis. The minimal lumen diameter, distal reference, proximal reference, reference diameter, percentage of diameter stenosis, and lesion length with the least foreshortening view were measured for the culprit lesion using standard techniques.15 The culprit lesion was determined based on the findings of coronary angiogram, electrocardiogram, transthoracic echocardiography, and/or myocardial perfusion scintigraphy.

OCT image acquisition

After the diagnostic angiography, an additional intravenous heparin at a dose of 100 U/kg was injected for the OCT examination. If the coronary flow was Thrombolysis In Myocardial Infarction grade 0, I, II, aspiration thrombectomy was performed using an aspiration catheter (Thrombuster II, Kaneka Corporation, Japan). After administration of intracoronary nitroglycerin (100–200 µg), OCT was performed before predilation with a balloon catheter for the culprit lesion. Images were acquired using the time-domain (M2 OCT Imaging System, LightLab Imaging, Westford, MA, USA) or frequency-domain (C7 OCT Intravascular Imaging System, St Jude Medical, St Paul, MN, USA) OCT system. The intracoronary OCT imaging technique has been described previously.16 Briefly, the M2 system uses a 3 F occlusion balloon catheter, and commercially available dextran was infused into the coronary artery from the distal tip of the occlusion balloon catheter at 0.5 mL/s by an injector to remove the blood from the field of view. The 0.016-inch imaging wire was automatically pulled back from a distal to a proximal position at a rate of 1.0 mm/s. In the C7 system, a 2.7 F OCT imaging catheter (Dragonfly, LightLab Imaging) was advanced distally to the lesion, and automatic pullback (at a rate of 20 mm/s) was initiated as soon as the blood was cleared by the injection of contrast media or dextran. All images were stored digitally for subsequent offline analysis.

OCT analysis

The OCT data were analysed using previously validated criteria for OCT plaque characterization.16 Each plaque was classified as a fibrous plaque or lipid plaque. When lipids were present in ≥90° of any of the cross-sectional images within the plaque, it was considered a lipid-rich plaque. In the lipid-rich plaque, the maximum lipid arc was measured. Lipid length was defined as the length of the segment with a lipid arc of ≥90° within the plaque and measured on longitudinal view. The fibrous cap thickness of the lipid-rich plaque was measured three times at its thinnest part, and the average value was calculated. The presence of plaque rupture, TCFA, macrophage infiltration, microvessels, calcification, and intracoronary thrombi was estimated. Plaque rupture was defined as the presence of fibrous cap discontinuity with cavity formation in the plaque. TCFA was considered when the fibrous cap thickness was ≤65 µm in the lipid-rich plaque on a cross-sectional image. Macrophage infiltration was defined as bright spots with high OCT backscattering signal variances. A microvessel was defined as a no-signal tubular structure without a connection to the vessel lumen recognized on three or more consecutive cross-sectional images in the M2 system and two or more consecutive cross-sectional images in the C7 system. Intracoronary thrombus was defined as a mass (diameter ≥ 250 µm) protruding into the lumen of the artery. Calcification was defined as an area with a low backscatter signal and a sharp border inside a plaque. The OCT findings were analysed by two experienced observers who were blinded to the angiographic and clinical presentations using proprietary computer software (LightLab Imaging) after confirming proper calibration settings of the Z-offset. When there was any discordance between the observers, a consensus reading was obtained. The intraclass correlation coefficients for inter- and intraobserver reliabilities of the fibrous cap thickness were 0.891 and 0.916, respectively.

Statistical analysis

Statistical analysis was performed using the JMP statistical software for Mac version 9.0.2 (SAS Institute, Inc., Cary, NC, USA). To analyse the HOMA-IR values as categorical variables, these levels were divided into tertiles: lower HOMA-IR (<1.40); middle HOMA-IR (1.40–2.50), and higher HOMA-IR (>2.50). Continuous variables were presented as mean ± standard deviation (SD); comparisons were performed using the one-way ANOVA and post hoc multiple comparison using Tukey–Kramer test or non-parametric Kruskal–Wallis test and post hoc multiple comparison using Steel–Dwass test for non-normally distributed variables. Categorical variables were presented as percentages and relative frequencies; comparisons were performed using the chi-square test, as appropriate. We performed simple linear regression analysis to determine the association between HOMA-IR values and thickness of fibrous cap. Levels of HOMA-IR did not distribute normally; therefore, transformed values of HOMA-IR in logarithm were used as variables for simple linear regression analysis. Univariate and multivariate logistic regression analyses were used to identify independent predictors of TCFA by adjusting for predefined variables. Several conventional and novel risk factors such as age, sex, ACS, body mass index (BMI), hypertension (HT), DM, HbA1c, LDL-cholesterol, statin use, high-sensitivity C-reactive protein (hs-C-reactive protein), and HOMA-IR were included in the multivariate model. A P-value <0.05 was considered statistically significant.

Results

Baseline characteristics

One hundred and fifty-five patients (68 ± 9 years, 114 men) were enrolled in this study. This study included 100 patients (65%) with SAP and 55 (35%) patients with ACS. One hundred patients (65%) had DM and the average HOMA-IR value was 2.38 ± 1.95 in this population. As described above, these subjects were divided into three groups according to tertiles (lower HOMA-IR, n = 51; middle HOMA-IR, n = 52; higher HOMA-IR, n = 52). The baseline characteristics of the three groups are shown in Table 1. There was no significant difference in the percentage of ACS among the three groups. There were no other significant clinical differences among the three groups except for the glucometabolic parameters, BMI, and triglyceride and LDL-cholesterol levels.

Table 1

Patient characteristics according to the HOMA-IR tertiles

 Lower tertile (n = 51) Middle tertile (n = 52) Higher tertile (n = 52) P-value 
Age, years 70 ± 9 66 ± 9 68 ± 9 0.14 
Male sex, n (%) 39 (77) 38 (73) 37 (71) 0.83 
ACS, n (%) 16 (31) 19 (37) 20 (39) 0.74 
Previous PCI, n (%) 16 (31) 16 (31) 19 (37) 0.79 
BMI, kg/mm2 22.0 ± 3.1* 25.5 ± 3.6 25.6 ± 3.0 <0.0001 
HT, n (%) 39 (77) 39 (75) 43 (83) 0.60 
Current smoker, n (%) 33 (65) 29 (56) 33 (64) 0.60 
DM, n (%) 28 (55) 33 (64) 39 (75) 0.10 
Laboratory data 
 eGFR, mL/min/1.73 m2 60.9 ± 27.8* 66.1 ± 20.3 67.3 ± 21.8 0.34 
 hs-C-reactive protein, mg/L 1.6 ± 1.8 1.3 ± 1.3 1.3 ± 1.0 0.82 
 LDL-cholesterol, mg/dL 100 ± 25 119 ± 32* 104 ± 30 0.003 
 HDL-cholesterol, mg/dL 45 ± 13* 41 ± 12 40 ± 8 0.26 
 Triglycerides, mg/dL 107 ± 48* 151 ± 66 148 ± 54 <0.0001 
 Fasting glucose, mg/dL 96 ± 16* 108 ± 19* 117 ± 22 <0.0001 
 Fasting insulin, IU/L 4.1 ± 1.3* 7.2 ± 2.1* 15.7 ± 9.9 <0.0001 
 HOMA-IR 1.0 ± 0.3* 1.8 ± 0.3* 4.3 ± 2.3 <0.0001 
 HbA1c, % 6.4 ± 1.2* 6.7 ± 1.0 7.1 ± 1.4 0.04 
Medication, n (%) 
 Aspirin 33 (65) 42 (81) 39 (75) 0.17 
 ACE-I/ARB 21 (41) 25 (48) 33 (64) 0.07 
 Statin 30 (59) 30 (58) 30 (58) 0.99 
 Oral glycerides 17 (33) 21 (40) 23 (44) 0.52 
 Sulphonyl urea 12 (24 13 (25) 17 (33) 0.53 
 Lower tertile (n = 51) Middle tertile (n = 52) Higher tertile (n = 52) P-value 
Age, years 70 ± 9 66 ± 9 68 ± 9 0.14 
Male sex, n (%) 39 (77) 38 (73) 37 (71) 0.83 
ACS, n (%) 16 (31) 19 (37) 20 (39) 0.74 
Previous PCI, n (%) 16 (31) 16 (31) 19 (37) 0.79 
BMI, kg/mm2 22.0 ± 3.1* 25.5 ± 3.6 25.6 ± 3.0 <0.0001 
HT, n (%) 39 (77) 39 (75) 43 (83) 0.60 
Current smoker, n (%) 33 (65) 29 (56) 33 (64) 0.60 
DM, n (%) 28 (55) 33 (64) 39 (75) 0.10 
Laboratory data 
 eGFR, mL/min/1.73 m2 60.9 ± 27.8* 66.1 ± 20.3 67.3 ± 21.8 0.34 
 hs-C-reactive protein, mg/L 1.6 ± 1.8 1.3 ± 1.3 1.3 ± 1.0 0.82 
 LDL-cholesterol, mg/dL 100 ± 25 119 ± 32* 104 ± 30 0.003 
 HDL-cholesterol, mg/dL 45 ± 13* 41 ± 12 40 ± 8 0.26 
 Triglycerides, mg/dL 107 ± 48* 151 ± 66 148 ± 54 <0.0001 
 Fasting glucose, mg/dL 96 ± 16* 108 ± 19* 117 ± 22 <0.0001 
 Fasting insulin, IU/L 4.1 ± 1.3* 7.2 ± 2.1* 15.7 ± 9.9 <0.0001 
 HOMA-IR 1.0 ± 0.3* 1.8 ± 0.3* 4.3 ± 2.3 <0.0001 
 HbA1c, % 6.4 ± 1.2* 6.7 ± 1.0 7.1 ± 1.4 0.04 
Medication, n (%) 
 Aspirin 33 (65) 42 (81) 39 (75) 0.17 
 ACE-I/ARB 21 (41) 25 (48) 33 (64) 0.07 
 Statin 30 (59) 30 (58) 30 (58) 0.99 
 Oral glycerides 17 (33) 21 (40) 23 (44) 0.52 
 Sulphonyl urea 12 (24 13 (25) 17 (33) 0.53 

Values represent mean ± standard deviation or n (%).

ACS, acute coronary syndrome; BMI, body mass index; HT, hypertension; HOMA-IR, homeostasis model assessment of insulin resistance; PCI, percutaneous coronary intervention; eGFR, estimated glomerular filtration rate; hs-C-reactive protein, high-sensitivity C-reactive protein; LDL, low-density lipoprotein; HDL, high-density lipoprotein; HbA1c, haemoglobin HbA1c; ACE-I, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker.

*P < 0.05 vs. patients in the higher tertile.

Angiographic results

The angiographic findings are summarized in Table 2. The distribution of culprit lesion location was similar among the three groups. The left anterior descending artery was the most frequent culprit lesion (46%), followed by the right coronary artery (39%) and the circumflex artery (15%). There were no significant differences in the frequency of multivessel disease and in the modified lesion classifications of the American College of Cardiology/American Heart Association. Based on quantitative coronary angiographic analysis, there were no statistical differences in the reference diameter, minimum lumen diameter, per cent diameter stenosis, and lesion length among the three groups.

Table 2

Angiographic analysis of the HOMA-IR tertiles

 Lower tertile (n = 51) Middle tertile (n = 52) Higher tertile (n = 52) P-value 
Lesion location, n (%) 
 Left anterior descending artery 23 (45) 27 (52) 21 (40) 0.65 
 Left circumflex artery 6 (12) 8 (15) 10 (19) 
 Right coronary artery 22 (43) 17 (33) 21 (40) 
Multivessel disease 23 (45) 15 (29) 25 (48) 0.10 
Modified ACC/AHA classification, n (%) 
 B2/C lesion 35 (69) 43 (83) 39 (75) 0.25 
Quantitative coronary analysis 
 Reference diameter, mm 2.61 ± 0.48 2.78 ± 0.63 2.56 ± 0.49 0.09 
 Minimum lumen diameter, mm 0.87 ± 0.33 0.82 ± 0.32 0.85 ± 0.35 0.77 
 Diameter stenosis, % 66.4 ± 12.1 70.5 ± 9.4 67.1 ± 10.7 0.12 
 Lesion length, mm 16.5 ± 7.8 15.2 ± 5.9 17.1 ± 8.1 0.51 
 Lower tertile (n = 51) Middle tertile (n = 52) Higher tertile (n = 52) P-value 
Lesion location, n (%) 
 Left anterior descending artery 23 (45) 27 (52) 21 (40) 0.65 
 Left circumflex artery 6 (12) 8 (15) 10 (19) 
 Right coronary artery 22 (43) 17 (33) 21 (40) 
Multivessel disease 23 (45) 15 (29) 25 (48) 0.10 
Modified ACC/AHA classification, n (%) 
 B2/C lesion 35 (69) 43 (83) 39 (75) 0.25 
Quantitative coronary analysis 
 Reference diameter, mm 2.61 ± 0.48 2.78 ± 0.63 2.56 ± 0.49 0.09 
 Minimum lumen diameter, mm 0.87 ± 0.33 0.82 ± 0.32 0.85 ± 0.35 0.77 
 Diameter stenosis, % 66.4 ± 12.1 70.5 ± 9.4 67.1 ± 10.7 0.12 
 Lesion length, mm 16.5 ± 7.8 15.2 ± 5.9 17.1 ± 8.1 0.51 

Values represent mean ± SD or n (%).

HOMA-IR, homeostasis model assessment of insulin resistance; ACC/AHA, American College of Cardiology/American Heart Association.

Results of OCT analysis

In the present study, there was no significant difference in use percentage of the C7 system among the three groups (lower HOMA-IR: 37%; middle HOMA-IR: 37%; higher HOMA-IR: 48%; P = 0.41). Table 3 and Figure 1 show the OCT findings of the three groups. Patients in the higher HOMA-IR tertile had more frequent prevalence of lipid-rich plaques than those in the middle and lower tertiles (83 vs. 62 vs. 57%; P = 0.01), and lipid length was significantly longer in the higher HOMA-IR group than in the other two tertiles (vs. lower HOMA-IR, P = 0.02; vs. middle HOMA-IR, P = 0.03). The prevalence rates of TCFA among the higher, middle, and lower HOMA-IR tertiles were 50, 29, and 26% (P = 0.02). In addition, the microvessel prevalence rates among the higher, middle, and lower HOMA-IR tertiles were 54, 39, and 28% (P = 0.02). Furthermore, in the higher HOMA-IR group, the fibrous cap was significantly thinner than in the other two tertiles (vs. lower HOMA-IR, P = 0.009; vs. middle HOMA-IR, P = 0.008). The representative OCT images of the coronary culprit lesions in the lower HOMA-IR group and the higher HOMA-IR group are shown in Figure 2. Overall, there was a weak correlation between log HOMA-IR level and fibrous cap thickness (r = −0.251, P = 0.008). In ACS, there was no significant correlation between log HOMA-IR and fibrous cap thickness (r = −0.158, P = 0.25), whereas log HOMA-IR significantly correlated with fibrous cap thickness in SAP (r = −0.370, P = 0.004) (Figure 3).

Table 3

OCT findings of the HOMA-IR tertiles

 Lower tertile (n = 51) Middle tertile (n = 52) Higher tertile (n = 52) P-value 
Minimal CSA, mm 1.6 ± 0.8 1.4 ± 0.8 1.5 ± 1.1 0.76 
Lipid-rich plaque, n (%) 29 (57) 32 (62) 43 (83) 0.01 
Calcification, n (%) 27 (53) 28 (54) 29 (56) 0.96 
Thrombus, n (%) 7 (14) 14 (27) 13 (25) 0.22 
Ruptured plaque, n (%) 8 (16) 6 (12) 8 (15) 0.80 
Macrophage infiltration, n (%) 15 (29) 20 (39) 26 (50) 0.10 
Microvessel, n (%) 14 (28) 20 (39) 28 (54) 0.02 
TCFA, n (%) 13 (26) 15 (29) 26 (50) 0.02 
Fibrous cap thickness, μm 95.5 ± 41.6* 89.0 ± 36.0* 69.7 ± 27.7 0.002 
Lipid arc, degree 119 ± 111 124 ± 97 143 ± 81 0.32 
Lipid length, mm 4.0 ± 5.2* 3.9 ± 4.1* 5.3 ± 3.7 0.04 
 Lower tertile (n = 51) Middle tertile (n = 52) Higher tertile (n = 52) P-value 
Minimal CSA, mm 1.6 ± 0.8 1.4 ± 0.8 1.5 ± 1.1 0.76 
Lipid-rich plaque, n (%) 29 (57) 32 (62) 43 (83) 0.01 
Calcification, n (%) 27 (53) 28 (54) 29 (56) 0.96 
Thrombus, n (%) 7 (14) 14 (27) 13 (25) 0.22 
Ruptured plaque, n (%) 8 (16) 6 (12) 8 (15) 0.80 
Macrophage infiltration, n (%) 15 (29) 20 (39) 26 (50) 0.10 
Microvessel, n (%) 14 (28) 20 (39) 28 (54) 0.02 
TCFA, n (%) 13 (26) 15 (29) 26 (50) 0.02 
Fibrous cap thickness, μm 95.5 ± 41.6* 89.0 ± 36.0* 69.7 ± 27.7 0.002 
Lipid arc, degree 119 ± 111 124 ± 97 143 ± 81 0.32 
Lipid length, mm 4.0 ± 5.2* 3.9 ± 4.1* 5.3 ± 3.7 0.04 

Values represent mean ± SD or n (%).

OCT, optical coherence tomography; TCFA, thin-cap fibroatheroma; HOMA-IR, homeostasis model assessment of insulin resistance; CSA, cross-sectional area.

*P < 0.05 vs. patients in the higher tertile.

Figure 1

Comparison of the presence of TCFA and fibrous cap thickness as assessed by OCT among the three HOMA-IR tertiles. The TCFA prevalence rates of the higher, middle, and lower HOMA-IR tertiles were 50, 29, and 26%, respectively (P = 0.02). In the higher HOMA-IR group, the fibrous cap was significantly thinner than in the other two tertiles (vs. lower HOMA-IR, P = 0.009; vs. middle HOMA-IR, P = 0.008). TCFA, thin-cap fibroatheroma; OCT, optical coherence tomography; HOMA-IR, homeostasis model assessment of insulin resistance.

Figure 1

Comparison of the presence of TCFA and fibrous cap thickness as assessed by OCT among the three HOMA-IR tertiles. The TCFA prevalence rates of the higher, middle, and lower HOMA-IR tertiles were 50, 29, and 26%, respectively (P = 0.02). In the higher HOMA-IR group, the fibrous cap was significantly thinner than in the other two tertiles (vs. lower HOMA-IR, P = 0.009; vs. middle HOMA-IR, P = 0.008). TCFA, thin-cap fibroatheroma; OCT, optical coherence tomography; HOMA-IR, homeostasis model assessment of insulin resistance.

Figure 2

Representative OCT images of coronary culprit lesions. The OCT images were obtained from culprit lesions of the left anterior descending artery in stable angina patients with diabetes. (A) Fibrous (Fib) and calcified plaques (Cal) in the lower HOMA-IR tertile (0.41). (B) Lipid-rich plaques (Lp), including TCFA (thinnest fibrous cap thickness: 60 µm, white arrow) and microvessels (triangle arrow) in the higher HOMA-IR tertile (4.04). Abbreviations are as in Figure 1.

Figure 2

Representative OCT images of coronary culprit lesions. The OCT images were obtained from culprit lesions of the left anterior descending artery in stable angina patients with diabetes. (A) Fibrous (Fib) and calcified plaques (Cal) in the lower HOMA-IR tertile (0.41). (B) Lipid-rich plaques (Lp), including TCFA (thinnest fibrous cap thickness: 60 µm, white arrow) and microvessels (triangle arrow) in the higher HOMA-IR tertile (4.04). Abbreviations are as in Figure 1.

Figure 3

Correlation between log HOMA-IR and fibrous cap thickness in SAP. There was a significant correlation between log HOMA-IR and fibrous cap thickness identified by OCT in SAP (r = −0.370, P = 0.004). r, correlation coefficient; SAP, stable angina pectoris; other abbreviations are as in Figure 1.

Figure 3

Correlation between log HOMA-IR and fibrous cap thickness in SAP. There was a significant correlation between log HOMA-IR and fibrous cap thickness identified by OCT in SAP (r = −0.370, P = 0.004). r, correlation coefficient; SAP, stable angina pectoris; other abbreviations are as in Figure 1.

Predictors of TCFA presence

Univariate analysis showed that only two factors, including ACS [odds ratio (OR): 10.04; 95% confidence interval (CI): 4.76–22.2; P < 0.0001] and HOMA-IR >2.5 (OR: 2.68; 95% CI: 1.34–5.41; P = 0.005), significantly related to the presence of TCFA. After adjusting for age, gender, BMI, ACS, and several risk factors such as HT, DM, HbA1c, LDL-cholesterol, statin use, and hs-C-reactive protein, multivariate logistic regression analysis for predicting the presence of TCFA revealed that ACS (OR: 17.98; 95% CI: 7.12−52.02; P < 0.0001) and HOMA-IR >2.50 (OR: 3.57; 95% CI: 1.42−9.55; P = 0.007) were independent factors for predicting the presence of TCFA. However, DM was not significantly associated with TCFA (OR: 2.55; 95% CI: 0.76−9.10; P = 0.13) (Table 4).

Table 4

Logistic regression analysis for predicting the presence of TCFA

Variable Odds ratio 95% confidence interval P-value 
ACS 17.98 7.12–52.02 <0.0001 
DM 2.55 0.76–9.10 0.13 
HOMA-IR (>2.50) 3.57 1.42–9.55 0.007 
LDL-cholesterol 1.01 0.99–1.03 0.38 
Statin use 0.62 0.21–1.77 0.37 
Triglyceride 1.01 0.99–1.01 0.20 
Hs-C-reactive protein 0.95 0.69–1.35 0.75 
Variable Odds ratio 95% confidence interval P-value 
ACS 17.98 7.12–52.02 <0.0001 
DM 2.55 0.76–9.10 0.13 
HOMA-IR (>2.50) 3.57 1.42–9.55 0.007 
LDL-cholesterol 1.01 0.99–1.03 0.38 
Statin use 0.62 0.21–1.77 0.37 
Triglyceride 1.01 0.99–1.01 0.20 
Hs-C-reactive protein 0.95 0.69–1.35 0.75 

Adjusted for risk factors (age, sex, BMI, HT, smoking, and HbA1c level).

ACS, acute coronary syndrome; DM, diabetes mellitus; TCFA, thin-cap fibroatheroma; HOMA-IR, homeostasis model assessment of insulin resistance; LDL, low-density lipoprotein; Hs-C-reactive protein, high-sensitivity C-reactive protein.

Discussion

The main findings of the present study were as follows: (i) patients in the higher HOMA-IR tertile had more frequent vulnerable plaque features such as lipid-rich plaque, TCFA, and microvessels as assessed by OCT than those in the middle and lower tertiles; in addition, the fibrous cap of patients in the higher IR tertile was significantly thinner than those in patients in the middle and lower tertiles; and (ii) higher HOMA-IR value (>2.50) predicted the presence of TCFA.

Several epidemiological studies have reported an association between insulin resistance and the prevalence of CAD. Amano et al.12 demonstrated that IB-IVUS-derived lipid-rich plaque rates were significantly associated with higher HOMA-IR tertiles in 172 consecutive patients with both SAP and ACS. Our results are in accordance with their study. On the other hand, Mitsuhashi et al.13 demonstrated that hyperinsulinaemia, defined as the calculated area under the insulin concentration–time curve, is associated with an increased lipid content measured by IB-IVUS and a greater plaque volume measured by conventional IVUS in non-culprit intermediate lesions in 82 non-diabetic patients with ACS, whereas there were no significant differences in the percentage of lipid area between the three HOMA-IR tertiles. We evaluated the lipid volume semi-quantitatively by measuring the presence of lipid-rich plaque, lipid arc, and length of the lipid-rich plaque due to the limited penetration depth of OCT. However, lipid-rich plaques might not be a major marker of plaque vulnerability. Since the first report of plaque rupture in 1844,17 many clinical investigators have noted vulnerable/unstable plaque features, which lead to ACS in vivo. As a widely recognized histological concept, a vulnerable plaque is characterized by the presence of TCFA, and defined as a large necrotic core with a thin fibrous cap (<65 µm) and increased macrophage infiltration.18 Currently, OCT is the best validated imaging modality for detecting vulnerable plaques, including TCFA and ruptured plaques. A recently published study demonstrated that insulin resistance calculated by HOMA-IR is an independent predictor of post-procedural myocardial injury and cardiovascular events after PCI with a drug-eluting stent.19 Another study revealed that the presence of OCT-defined TCFA predicts post-procedural MI in PCI patients.20 Our present study might suggest complementary relationships among insulin resistance, vulnerable plaque, and cardiovascular events after PCI. In addition, our present study demonstrated that insulin resistance was significantly associated with the presence of microvessel as identified by OCT. Intraplaque microvessels play a pivotal role in coronary plaque growth by increasing red blood cells, thereby supplying inflammatory cells and cytokines into the plaque. Sluimer et al.21 revealed that microvessel was increased in advanced plaques compared with early plaques, and the microvessel mural cell coverage was incomplete in normal and atherosclerotic arteries in a human histological study. They suggested that the compromised structural integrity of intraplaque angiogenesis might explain the microvascular leakage responsible for extensive leucocyte infiltration, intraplaque haemorrhage, and plaque instability. Furthermore, a recent clinical study described that TCFA and microvessels identified by OCT were the potential predictors of subsequent progression of coronary plaques.22 These results support our hypothesis about insulin resistance and plaque instability. To the best of our knowledge, this is the first report to evaluate the association between insulin resistance and coronary plaque components assessed by OCT.

Although the concept that systemic/local inflammation accelerates the progression of atherosclerotic plaque has been well established in a large number of previous studies,23,24 the relationships among insulin resistance, inflammation, and plaque instability are intricately intertwined pathophysiologically. Elevated levels of pro-inflammatory cytokines such as tumour necrosis factor α, interleukin (IL)-6, IL-1β, and monocyte chemoattractant protein-1 have been shown in individuals with insulin resistance and DM.25–27 These cytokines play important roles in the activation of macrophage foam cells, resulting in early plaque development and fibrous cap weakening. Our results demonstrated that macrophage infiltration as assessed by OCT tended to be more frequently present in the higher HOMA-IR tertile. However, our present results could not demonstrate a significant association between insulin resistance and hs-C-reactive protein level—a clinical biomarker of inflammation. C-reactive protein levels correlate well with known risk factors such as smoking, low levels of HDL-cholesterol, and obesity.28 In addition, although we excluded the subjects with active inflammatory conditions from the present study, C-reactive protein—an acute-phase reactant produced by the liver upon stimulation by IL-6—is a non-specific indicator of inflammation and thus may not directly influence atherogenesis.29

Although DM is a well-known risk factor for the development of cardiovascular events, the previous studies using OCT imaging reported conflicting results as to whether continuous hyperglycaemia was associated with coronary plaque vulnerability.5,30,31 In the present study, both the presence of DM and HbA1c level were not significantly associated with TCFA. There are potential reasons for these results. We excluded diabetic patients receiving insulin therapy who have a higher risk of in-stent restenosis and cardiovascular events than those who were treated with oral glycerides,32 because it is difficult to assess insulin resistance in subjects receiving insulin therapy. In addition, we excluded subjects with fasting plasma glucose level >140 mg/dL from this study. There is general consensus that HOMA model is incredible when fasting glucose level is >140 mg/dL, because basal fasting insulin production becomes less responsive to increasing glucose production of >140–160 mg/dL.33 Indeed, it has been reported that HOMA-IR showed lower correlation with M-value, insulin sensitivity as the average rate of exogenous glucose infusion for 30 min, by using the euglycaemic hyperinsulinaemic clamp technique in type 2 diabetic patients with moderate hyperglycaemia (fasting plasma glucose >140 mg/dL).34 Although we adopted strict inclusion criteria in the present study for reliable assessment of insulin resistance, we speculate these factors might attenuate the influence of DM itself for plaque instability. Furthermore, the prognostic value of HbA1c, which is an indicator of the average blood glucose concentrations over the preceding 2–3 months, in CAD remains controversial.35,36

The present study revealed a significant association between insulin resistance and coronary plaque vulnerability. Additional treatment with insulin sensitizer may be a suitable approach for preventing future cardiovascular events.

Study limitations

This study has several limitations. First, this study included patients with both SAP and ACS. As shown in this study, although the presence of ACS is the strongest confounding factor related to vulnerable plaques, there were no differences in the percentage of ACS among the three groups. Furthermore, multivariate analysis including ACS revealed that higher HOMA-IR values were independently related to the presence of TCFA. Second, we assessed the plaque component using both M2 and C7 OCT systems in this study. Although the use percentage of the C7 system did not significantly differ among the three groups, potential differences in the ability to detect tissue characteristics, including lipid content, TCFA, and microvessels (due to difference of frame spacing between M2 and C7), might have affected the plaque classification of the three groups. Third, intracoronary thrombi were observed in many cases with ACS. Although we excluded lesions with a large quantity of thrombi from the OCT analysis and we performed aspiration thrombectomy for lesions with delayed coronary flow, these thrombi might have affected analysis of the plaque left behind. Fourth, as we described above, the true effect of DM against plaque vulnerability cannot be properly assessed using the present study design and population (including only mild or well­controlled DM). Finally, this study was conducted at a single centre with a relatively small sample size. Further large-scale studies are warranted to determine whether insulin resistance as defined by HOMA-IR may predict coronary plaque progression.

Conclusions

This study demonstrated that insulin resistance assessed by HOMA-IR is independently related to coronary plaque vulnerability identified by OCT. Insulin resistance assessed by HOMA-IR might be a useful marker for risk stratification in patients with CAD.

Conflict of interest: None declared.

Funding

No authors have support or sponsors.

References 

1
Haffner
SM
Lehto
S
Ronnemaa
T
Pyorala
K
Laakso
M
Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction
N Engl J Med
 , 
1998
, vol. 
339
 (pg. 
229
-
34
)
2
Norhammar
A
Malmberg
K
Diderholm
E
Lagerqvist
B
Lindahl
B
Ryden
L
, et al.  . 
Diabetes mellitus: the major risk factor in unstable coronary artery disease even after consideration of the extent of coronary artery disease and benefits of revascularization
J Am Coll Cardiol
 , 
2004
, vol. 
43
 (pg. 
585
-
91
)
3
Otter
W
Kleybrink
S
Doering
W
Standl
E
Schnell
O
Hospital outcome of acute myocardial infarction in patients with and without diabetes mellitus
Diabet Med
 , 
2004
, vol. 
21
 (pg. 
183
-
7
)
4
Burke
AP
Kolodgie
FD
Zieske
A
Fowler
DR
Weber
DK
Varghese
PJ
, et al.  . 
Morphologic findings of coronary atherosclerotic plaques in diabetics: a postmortem study
Arterioscler Thromb Vasc Biol
 , 
2004
, vol. 
24
 (pg. 
1266
-
71
)
5
Kato
K
Yonetsu
T
Kim
SJ
Xing
L
Lee
H
McNulty
I
, et al.  . 
Comparison of nonculprit coronary plaque characteristics between patients with and without diabetes: a 3-vessel optical coherence tomography study
JACC Cardiovasc Interv
 , 
2012
, vol. 
5
 (pg. 
1150
-
8
)
6
Moreno
PR
Murcia
AM
Palacios
IF
Leon
MN
Bernardi
VH
Fuster
V
, et al.  . 
Coronary composition and macrophage infiltration in atherectomy specimens from patients with diabetes mellitus
Circulation
 , 
2000
, vol. 
102
 (pg. 
2180
-
4
)
7
Nasu
K
Tsuchikane
E
Katoh
O
Fujita
H
Surmely
JF
Ehara
M
, et al.  . 
Plaque characterisation by virtual histology intravascular ultrasound analysis in patients with type 2 diabetes
Heart
 , 
2008
, vol. 
94
 (pg. 
429
-
33
)
8
Breen
DM
Giacca
A
Effects of insulin on the vasculature
Curr Vasc Pharmacol
 , 
2011
, vol. 
9
 (pg. 
321
-
32
)
9
Despres
JP
Lamarche
B
Mauriege
P
Cantin
B
Dagenais
GR
Moorjani
S
, et al.  . 
Hyperinsulinemia as an independent risk factor for ischemic heart disease
N Engl J Med
 , 
1996
, vol. 
334
 (pg. 
952
-
7
)
10
Hanley
AJ
Williams
K
Stern
MP
Haffner
SM
Homeostasis model assessment of insulin resistance in relation to the incidence of cardiovascular disease: the San Antonio Heart Study
Diabetes Care
 , 
2002
, vol. 
25
 (pg. 
1177
-
84
)
11
An
X
Yu
D
Zhang
R
Zhu
J
Du
R
Shi
Y
, et al.  . 
Insulin resistance predicts progression of de novo atherosclerotic plaques in patients with coronary heart disease: a one-year follow-up study
Cardiovasc Diabetol
 , 
2012
, vol. 
11
 pg. 
71
 
12
Amano
T
Matsubara
T
Uetani
T
Nanki
M
Marui
N
Kato
M
, et al.  . 
Abnormal glucose regulation is associated with lipid-rich coronary plaque: relationship to insulin resistance
JACC Cardiovasc Imaging
 , 
2008
, vol. 
1
 (pg. 
39
-
45
)
13
Mitsuhashi
T
Hibi
K
Kosuge
M
Morita
S
Komura
N
Kusama
I
, et al.  . 
Relation between hyperinsulinemia and nonculprit plaque characteristics in nondiabetic patients with acute coronary syndromes
JACC Cardiovasc Imaging
 , 
2011
, vol. 
4
 (pg. 
392
-
401
)
14
Matthews
DR
Hosker
JP
Rudenski
AS
Naylor
BA
Treacher
DF
Turner
RC
Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man
Diabetologia
 , 
1985
, vol. 
28
 (pg. 
412
-
9
)
15
Reiber
JH
Serruys
PW
Kooijman
CJ
Wijns
W
Slager
CJ
Gerbrands
JJ
, et al.  . 
Assessment of short-, medium-, and long-term variations in arterial dimensions from computer-assisted quantitation of coronary cineangiograms
Circulation
 , 
1985
, vol. 
71
 (pg. 
280
-
8
)
16
Prati
F
Regar
E
Mintz
GS
Arbustini
E
Di Mario
C
Jang
IK
, et al.  . 
Expert review document on methodology, terminology, and clinical applications of optical coherence tomography: physical principles, methodology of image acquisition, and clinical application for assessment of coronary arteries and atherosclerosis
Eur Heart J
 , 
2010
, vol. 
31
 (pg. 
401
-
15
)
17
Herrick
JB
Landmark article (JAMA 1912). Clinical features of sudden obstruction of the coronary arteries. By James B. Herrick
JAMA
 , 
1983
, vol. 
250
 (pg. 
1757
-
65
)
18
Virmani
R
Kolodgie
FD
Burke
AP
Farb
A
Schwartz
SM
Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions
Arterioscler Thromb Vasc Biol
 , 
2000
, vol. 
20
 (pg. 
1262
-
75
)
19
Uetani
T
Amano
T
Harada
K
Kitagawa
K
Kunimura
A
Shimbo
Y
, et al.  . 
Impact of insulin resistance on post-procedural myocardial injury and clinical outcomes in patients who underwent elective coronary interventions with drug-eluting stents
JACC Cardiovasc Interv
 , 
2012
, vol. 
5
 (pg. 
1159
-
67
)
20
Lee
T
Yonetsu
T
Koura
K
Hishikari
K
Murai
T
Iwai
T
, et al.  . 
Impact of coronary plaque morphology assessed by optical coherence tomography on cardiac troponin elevation in patients with elective stent implantation
Circ Cardiovasc Interv
 , 
2011
, vol. 
4
 (pg. 
378
-
86
)
21
Sluimer
JC
Kolodgie
FD
Bijnens
AP
Maxfield
K
Pacheco
E
Kutys
B
, et al.  . 
Thin-walled microvessels in human coronary atherosclerotic plaques show incomplete endothelial junctions relevance of compromised structural integrity for intraplaque microvascular leakage
J Am Coll Cardiol
 , 
2009
, vol. 
53
 (pg. 
1517
-
27
)
22
Uemura
S
Ishigami
K
Soeda
T
Okayama
S
Sung
JH
Nakagawa
H
, et al.  . 
Thin-cap fibroatheroma and microchannel findings in optical coherence tomography correlate with subsequent progression of coronary atheromatous plaques
Eur Heart J
 , 
2012
, vol. 
33
 (pg. 
78
-
85
)
23
Carter
AM
Inflammation, thrombosis and acute coronary syndromes
Diab Vasc Dis Res
 , 
2005
, vol. 
2
 (pg. 
113
-
21
)
24
Ikeda
U
Inflammation and coronary artery disease
Curr Vasc Pharmacol
 , 
2003
, vol. 
1
 (pg. 
65
-
70
)
25
Mita
T
Goto
H
Azuma
K
Jin
WL
Nomiyama
T
Fujitani
Y
, et al.  . 
Impact of insulin resistance on enhanced monocyte adhesion to endothelial cells and atherosclerogenesis independent of LDL cholesterol level
Biochem Biophys Res Commun
 , 
2010
, vol. 
395
 (pg. 
477
-
83
)
26
Olefsky
JM
Glass
CK
Macrophages, inflammation, and insulin resistance
Annu Rev Physiol
 , 
2010
, vol. 
72
 (pg. 
219
-
46
)
27
Deepa
R
Velmurugan
K
Arvind
K
Sivaram
P
Sientay
C
Uday
S
, et al.  . 
Serum levels of interleukin 6, C-reactive protein, vascular cell adhesion molecule 1, and monocyte chemotactic protein 1 in relation to insulin resistance and glucose intolerance—the Chennai Urban Rural Epidemiology Study (CURES)
Metabolism
 , 
2006
, vol. 
55
 (pg. 
1232
-
8
)
28
Miller
M
Zhan
M
Havas
S
High attributable risk of elevated C-reactive protein level to conventional coronary heart disease risk factors: the Third National Health and Nutrition Examination Survey
Arch Intern Med
 , 
2005
, vol. 
165
 (pg. 
2063
-
8
)
29
Scirica
BM
Morrow
DA
Is C-reactive protein an innocent bystander or proatherogenic culprit? The verdict is still out
Circulation
 , 
2006
, vol. 
113
 (pg. 
2128
-
34
Discussion 2151
30
Chia
S
Raffel
OC
Takano
M
Tearney
GJ
Bouma
BE
Jang
IK
Comparison of coronary plaque characteristics between diabetic and non-diabetic subjects: an in vivo optical coherence tomography study
Diabetes Res Clin Pract
 , 
2008
, vol. 
81
 (pg. 
155
-
60
)
31
Feng
T
Yundai
C
Lian
C
Zhijun
S
Changfu
L
Jun
G
, et al.  . 
Assessment of coronary plaque characteristics by optical coherence tomography in patients with diabetes mellitus complicated with unstable angina pectoris
Atherosclerosis
 , 
2010
, vol. 
213
 (pg. 
482
-
5
)
32
Kumar
R
Lee
TT
Jeremias
A
Ruisi
CP
Sylvia
B
Magallon
J
, et al.  . 
Comparison of outcomes using sirolimus-eluting stenting in diabetic versus nondiabetic patients with comparison of insulin versus non-insulin therapy in the diabetic patients
Am J Cardiol
 , 
2007
, vol. 
100
 (pg. 
1187
-
91
)
33
DeFronzo
RA
Ferrannini
E
Simonson
DC
Fasting hyperglycemia in non-insulin-dependent diabetes mellitus: contributions of excessive hepatic glucose production and impaired tissue glucose uptake
Metabolism
 , 
1989
, vol. 
38
 (pg. 
387
-
95
)
34
Matsuhisa
M
Yamasaki
Y
Emoto
M
Shimabukuro
M
Ueda
S
Funahashi
T
, et al.  . 
A novel index of insulin resistance determined from the homeostasis model assessment index and adiponectin levels in Japanese subjects
Diabetes Res Clin Pract
 , 
2007
, vol. 
77
 (pg. 
151
-
4
)
35
Gerstein
HC
Miller
ME
Byington
RP
Goff
DC
Jr
Bigger
JT
Buse
JB
, et al.  . 
Effects of intensive glucose lowering in type 2 diabetes
N Engl J Med
 , 
2008
, vol. 
358
 (pg. 
2545
-
59
)
36
Liu
Y
Yang
YM
Zhu
J
Tan
HQ
Liang
Y
Li
JD
Prognostic significance of hemoglobin A1c level in patients hospitalized with coronary artery disease. A systematic review and meta-analysis
. Cardiovasc Diabetol
 , 
2011
, vol. 
10
 pg. 
98
 

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