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Dynamic natural morphologies and component changes in nonculprit subclinical atherosclerosis in patients with acute coronary syndrome at 1-year follow-up and clinical significance at 3-year follow-up

      Highlights

      • Regression of nonculprit subclinical atherosclerosis at 1-year was mainly caused by regression of the lipid component.
      • The degree of LDL-C reduction was positively correlated with the regression of nonculprit subclinical atherosclerosis.
      • Regression of nonculprit subclinical atherosclerosis at 1-year follow-up predicted decrease of MACE at 3-year follow-up.

      Abstract

      Background and aims

      We aimed to explore the dynamic natural morphologies and main components of nonculprit subclinical atherosclerotic changes underlying lesion regression (LR) or lesion progression (LP) in patients with acute coronary syndrome.

      Methods

      The primary endpoints were changes in percent atheroma volume (ΔPAV), normalized total atheroma volume (ΔTAVn) and each component in nonculprit subclinical atherosclerosis from baseline to 1 year measured by optical flow ratio (OFR) software. LR or LP was defined by an increase or decrease in PAV. Secondary endpoints included the correlation between changes in the lipid profile and ΔPAV/ΔTAVn and major adverse cardiac events (MACEs) related to nonculprit subclinical atherosclerosis at 3 years.

      Results

      This was a subgroup analysis of our previous randomized trial with a total of 161 nonculprit lesions analysed. In the LR (approximately 55.3% of the lesions) group, ΔTAVn was positively correlated only with lipid ΔTAVn (r = 0.482, p < 0.001) but not fibrous and calcium ΔTAVn, and ΔPAV was positively correlated with lipid ΔPAV (r = 0.315, p = 0.003) but not fibrous and calcium ΔPAV. The percent reduction in low-density lipoprotein cholesterol (LDL-C) was an independent predictor of LR in multivariate logistic regression analysis (OR = 3.574, 95% CI: 1.125–11.347, p = 0.031). The incidence of MACEs related to nonculprit lesions at 3 years was higher in the LP group than the LR group (9.9% vs. 2.2%, p = 0.040).

      Conclusions

      LR of nonculprit subclinical atherosclerosis at 1-year follow-up was mainly caused by regression of the lipid component, which was correlated with the degree of LDL-C reduction and fewer MACEs at 3-year follow-up.

      Graphical abstract

      Keywords

      1. Introduction

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      ], some studies showed that similar lipid reduction did not result in the same degrees of plaque regression based on changes in normalized total atheroma volume (ΔTAVn) and/or percent atheroma volume (ΔPAV) measurements [
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      ]. Due to the limitations of various IVI technological features, most studies of plaque regression or progression are conducted with intravascular ultrasound (IVUS) alone or in combination with near-infrared spectroscopy (NIRS) [
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      • Shimada K.
      • Kurata T.
      • et al.
      Early statin treatment in patients with acute coronary syndrome: demonstration of the beneficial effect on atherosclerotic lesions by serial volumetric intravascular ultrasound analysis during half a year after coronary event: the ESTABLISH study.
      ,
      • Hattori K.
      • Ozaki Y.
      • Ismail T.F.
      • Okumura M.
      • Naruse H.
      • et al.
      Impact of statin therapy on plaque characteristics as assessed by serial OCT, grayscale and integrated backscatter-IVUS, JACC Cardiovasc.
      ,
      • Park S.J.
      • Kang S.J.
      • Ahn J.M.
      • Chang M.
      • Yun S.C.
      • et al.
      Effect of statin treatment on modifying plaque composition: a double-blind, randomized study.
      ,
      • Koskinas K.C.
      • Mach F.
      • Räber L.
      Lipid-lowering therapy and percutaneous coronary interventions.
      ,
      • Blaha M.J.
      • Daubert M.A.
      Assessing the impact of coronary plaque on the relative and absolute risk reduction with statin therapy.
      ,
      • Nicholls S.J.
      • Puri R.
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      ]. Although optical coherence tomography (OCT) has higher resolution, it is often unable to accurately distinguish the external elastic membrane behind the plaque due to its weak penetrating power, so the area and volume of the plaque cannot be quantified by OCT measurement; most studies of plaque outcome assessed by OCT measurement are based on changes in fibrous cap thickness or maximum lipid arc rather than plaque volume or components [
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      ]. Recently, a novel artificial intelligence framework, the optical flow ratio (OFR), was created for clinical use. This framework provides automatic plaque characterization and coronary physiological functional analysis software (Pulse Medical Imaging Technology, Shanghai, Co., Ltd) based on OCT data [
      • Chu M.
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      ]. The purpose of this post hoc analysis was to analyse the changes in plaque components in nonculprit subclinical atherosclerosis and their correlation with changes in lipid profiles. Further differences in 3-year MACEs related to nonculprit lesions were followed up.

      2. Patients and methods

      2.1 Study design and patient selection

      In this subgroup analysis study, which was based on our previous randomized study of OFR analysis for nonculprit subclinical atherosclerotic component changes, baseline and follow-up data were compared (ClinicalTrials.gov. Number: NCT02140801) [
      • Wu X.
      • You W.
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      ]. The study was approved by the institutional review board, and written informed consent was obtained from all patients. Between May 2014 and March 2018, 352 Chinese patients with ACS who underwent percutaneous coronary intervention (PCI) treatment for culprit lesions guided by OCT entered the preliminary screening phase of this study. The exclusion criteria were incomplete OCT data, low-quality OCT images, images that could not be analysed by OFR, lack of clinical or OCT follow-up data, and absence of nonculprit subclinical atherosclerosis. Finally, 161 lesions in 161 patients who completed the 3-year clinical follow-up were included in the OFR analysis. If a nonculprit subclinical atherosclerotic lesion was located in a PCI-treated vessel, the distance between the stent edge and the analysed segment had to be more than 10 mm. Finally, OFR analysis of all effectively selected cases at baseline and follow-up was performed by two independent technicians who had no knowledge of the patients’ clinical presentation. Before performing OFR, all technicians obtained a certificate after training and were recognized by Pulse Medical Imaging Technology, Shanghai, China.
      All patients were treated with dual antiplatelet therapy (including aspirin 100 mg/d + clopidogrel 75 mg/d or ticagrelor 90 mg bid) and switched to aspirin single antiplatelet therapy 12 months post-PCI; moreover, all patients received long-term statin therapy at a conventional dose based on the doctors’ discretion, which included atorvastatin (78 cases (48.5%), the average dose was 19.6 ± 1.9 mg), rosuvastatin (77 cases (47.8%), the average dose was 10.0 ± 0.0 mg), and simvastatin (6 cases (3.7%), the average dose was 20.0 ± 0.0 mg). Of 54 (33.5%) patients treated with ezetimibe, no patients received proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors. The treatment of other risk factors for coronary heart disease is shown in Table 1. If there were no serious complications, the treatment plan was not changed. Baseline data, including preoperative lipid profiles (total cholesterol, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C), and triglycerides), were collected, and the same indexes were reviewed at the 1-year follow-up while considering the difference in the lipid profile from baseline to the target value. Changes in LDL-C (ΔLDL-C) were defined as the LDL-C level at the 1-year follow-up minus the LDL-C level at baseline, and percent changes in LDL-C (ΔLDL-C%) were calculated as ΔLDL-C/LDL-C level at baseline × 100%.
      Table 1Baseline characteristics of the patients and lipid profiles.
      Total (N = 161)LR group (n = 89)LP group (n = 72)p value between groups
      Age, years70.00 [61.00, 77.00]69.00 [59.00, 75.00]72.00 [64.25, 78.75]0.139
      Men, n (%)122 (75.8)63 (70.8)59 (81.9)0.100
      BMI24.93 ± 2.8625.13 ± 2.8324.68 ± 2.900.330
      Hypertension, n (%)109 (67.7)62 (69.7)47 (65.3)0.554
      Dyslipidaemia, n (%)106 (65.8)58 (65.2)48 (66.7)0.842
      Diabetes, n (%)44 (27.3)17 (19.1)27 (37.5)0.009
      Smoking, n (%)52 (32.3)28 (31.5)24 (33.3)0.454
      Previous PCI, n (%)23 (14.3)13 (14.6)10 (13.9)0.897
      Baseline medical therapy
       Statin, n (%)161 (100.0)89 (100.0)72 (100.0)NA
       Ezetimibe, n (%)54 (33.5)26 (29.2)28 (38.9)0.196
       Antiplatelet therapy, n (%)161 (62.7)89 (100.0)72 (100.0)NA
       ACEI/ARB, n (%)95 (59.0)59 (66.3)36 (50.0)0.037
       CCB, n (%)44 (27.3)26 (29.2)18 (25.0)0.551
       Beta-blocker, n (%)86 (53.4)54 (60.7)32 (44.4)0.040
      Baseline lipid profile
       Total cholesterol, mmol/L3.72 [3.06, 4.54]3.77 [3.26, 4.59]3.51 [2.99, 4.45]0.158
       LDL-C, mmol/L2.04 [1.66, 2.80]2.08 [1.68, 2.84]1.91 [1.61, 2.80]0.166
       HDL-C, mmol/L0.99 [0.87, 1.20]0.99 [0.88, 1.21]0.98 [0.87, 1.21]0.904
       Triglycerides, mmol/L1.40 [1.02, 1.95]1.41 [1.04, 1.78]1.38 [0.93, 2.15]0.951
      Change in lipid profile between index and follow-up
       ΔTotal cholesterol, mmol/L−0.22 [-0.98, 0.50]−0.47 [-1.12, 0.12]0.08 [-0.61, 0.70]0.001
       ΔLDL-C, mmol/L−0.35 [-0.15, 0.87]−0.49 [-1.10, −0.01]−0.08 [-0.76, 0.32]0.001
       ΔLDL-C%−18.17 [-35.04,9.13]−22.34 [-36.90, −0.63]−4.97 [-29.80,17.02]0.003
       ΔHDL-C, mmol/L-0.14 [-0.25, −0.02]0.11 [0.01, 0.24]0.17 [0.04,0.28]0.189
       ΔTriglycerides, mmol/L−0.18 [-0.15, 0.61]−0.26 [-0.61, 0.12]−0.09 [-0.64, 0.19]0.275
      Values are expressed as the median (interquartile range) for continuous variables with abnormal distribution or frequency (percentage) for categorical variables in the table. ACE, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; CCB, calcium channel blocker; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LP, lesion progression; LR, lesion regression; OCT, optical coherence tomography; PCI, percutaneous coronary intervention.

      2.2 OCT image acquisition and analysis

      OCT images were acquired after intracoronary nitroglycerine injection. Both ILUMIEN OPTIS and C7-XR (Lightlab Imaging Incorporated, Westford, MA) could be applied with a 2.7F (Dragonfly OPTIS or Dragonfly Duo imaging catheter, Westford, MA) catheter with automatic pullback at a speed of 36 mm/s with continuous contrast injection (3–4 ml/s) to remove blood cells. OCT images were analysed offline using a dedicated software (OctPlus, version V2, Pulse Medical, Shanghai, China) with artificial intelligence algorithms [
      • Chu M.
      • Jia H.
      • Gutiérrez-Chico J.L.
      • Maehara A.
      • Ali Z.A.
      • et al.
      Artificial intelligence and optical coherence tomography for the automatic characterisation of human atherosclerotic plaques.
      ,
      • Zeng X.
      • Holck E.N.
      • Westra J.
      • Hu F.
      • Huang J.
      • et al.
      Impact of coronary plaque morphology on the precision of computational fractional flow reserve derived from optical coherence tomography imaging.
      ]. Quantitative planimetry measurements were obtained by automatic border detection followed by manual correction frame by frame. Finally, the following results were obtained: total atheroma volume (TAV), percent atheroma volume (PAV), lipid TAV, lipid PAV, fibrous TAV, fibrous PAV, calcium TAV, calcium PAV, macrophage TAV, macrophage PAV, and thinnest fibrous cap thickness (TFCT) in the analysed segment. The calculation of normalized TAV (TAVn) was similar to that described by a previous study [
      • Nicholls S.J.
      • Puri R.
      • Anderson T.
      • Ballantyne C.M.
      • Cho L.
      • et al.
      Effect of evolocumab on progression of coronary disease in statin-treated patients: the GLAGOV randomized clinical trial.
      ]: TAVn = TAV/number of frames of target segment × 100, where the average atheroma plaque area in each frame was analysed in the entire cohort to compensate for differences in segment length among patients. Changes in TAV (ΔTAVn) from baseline to 1-year follow-up were calculated as the TAVn at the 1-year follow-up minus the TAVn at baseline. Similar calculations were performed for the change in PAV (ΔPAV), which was calculated as the PAV at the 1-year follow-up minus the PAV at baseline. LR was defined as any decrease in PAV from baseline based on a previous study, while LP was defined as an increase in PAV [
      • Nicholls S.J.
      • Puri R.
      • Anderson T.
      • Ballantyne C.M.
      • Cho L.
      • et al.
      Effect of evolocumab on progression of coronary disease in statin-treated patients: the GLAGOV randomized clinical trial.
      ].

      2.3 Study endpoints

      The primary observational endpoints were the correlations of ΔTAVn/ΔPAV with lipid ΔTAVn/ΔPAV, fibrous ΔTAVn/ΔPAV, calcium ΔTAVn/ΔPAV, and macrophage ΔTAVn/ΔPAV in the overall data. The other observational endpoints were the correlations of ΔTAVn/ΔPAV with lipid ΔTAVn/ΔPAV, fibrous ΔTAVn/ΔPAV, calcium ΔTAVn/ΔPAV, and macrophage ΔTAVn/ΔPAV in the LP and LR groups, respectively. The correlation of changes in the lipid profile and ΔTAVn/ΔPAV with changes in each component and MACEs (defined as the composite measure of cardiac death, myocardial infarction, or ischaemia-driven revascularization) related to nonculprit lesions at 3-year follow-up were compared between the LR and LP groups.

      2.4 Manual coregistration of angiography and OCT at baseline and follow-up

      Angiography and OCT images at baseline and 1-year follow-up should achieve manual multiaspect coregistration based on reproducible index side branches and known pullback speeds, which can be measured by the quantified frame number. The study vessel segment for measurement was defined as at least 15 mm of the maximal plaque-containing region (plaque burden of ≥30%) post manual coregistration (side branch diameters ≥2 mm should be avoided, including in the segment of interest, as much as possible). The proximal and distal sites of the analysed segment were defined by calibration with a reproducible index side branch.

      2.5 Statistical analysis

      Before final analysis of all data, intraobserver and interobserver variabilities in the image analysis were assessed for 50 randomly selected images by Kappa statistics for plaque characteristic qualitative analysis of categorical variables (such as lipid, fibrous, and calcium plaque) and by intraclass correlation coefficients (ICC) for continuous variables (such as TAV, PAV and TFCT) by the same technician at a 2-week interval or by two independent technicians.
      All statistical analyses were performed in SPSS version 26.0 software (IBM, Armonk, New York), and box diagrams were drawn using R version 4.0.4 software (R Foundation for Statistical Computing, Vienna, Austria). For continuous variables with a normal distribution confirmed using the Shapiro–Wilk test, means and standard deviations are reported, and analysis of variance was used for comparisons between groups. For variables with a nonnormal distribution, medians and interquartile ranges (IQRs) are reported, and the rank-sum test was used for comparisons between groups. Count data are described by case number and constituent ratio n (%), and the χ2 test was used for comparisons between groups. Variables with statistical significance were included in multivariate logistic regression to analyse prognostic factors. Estimated MACE-free survival was generated by Kaplan–Meier analysis, and comparisons were performed by the log-rank test. A two-sided p value < 0.05 indicated statistical significance.

      3. Results

      3.1 Patients’ clinical characteristics and medical treatment

      Clinical characteristics, such as clinical risk factors and lipid profiles, at baseline and 1-year follow-up are summarized in Table 1. The average age was 70 years (61.00, 77.00), and 75.8% of the patients were male. The prevalence of diabetes mellitus was 27.3% (19.1% in the LR group and 37.5% in the LP group), and the prevalence of a previous dyslipidaemia history was 65.8%, with 2/3 of patients receiving statin therapy for at least one month. All patients underwent long-term treatment including aspirin and a P2Y12 inhibitor (clopidogrel or ticagrelor according to the surgeon's discretion) post-PCI and were maintained on this regimen for at least 12 months before switching to single aspirin antiplatelet therapy. Statin therapy for lipid control was given to all patients and combined with ezetimibe for 33.5% of patients. In general, LDL-C levels significantly decreased from baseline to 1-year follow-up (2.04 (1.65, 2.80) vs. 1.70 (1.41, 2.14), p < 0.001); LDL-C levels decreased from 2.08 to 1.61 mmol/L in the LR group (p < 0.05) and from 1.91 to 1.82 mmol/L in the LP group (p > 0.05), and the difference between groups was significant (p < 0.001). A higher proportion of patients in the LR group achieved a final LDL-C level less than 1.4 mmol/L (37.1% vs. 22.2%, p = 0.042), but the difference in the proportion of patients achieving an LDL level less than 1.0 mmol/L between the LR and LP groups at 1-year follow-up was not significant (4.5% vs. 6.9%, p = 0.501).

      3.2 Reproducibility analysis of OCT measurements

      There was good intraobserver and interobserver agreement for the assessment of categorical variables, such as lipid plaque, fibrous plaque, and calcium plaque (kappa >0.9), by OCT detection and continuous variables (such as PAV, TAV, TFCT) by OFR measurement (all ICC >0.9).

      3.3 OCT measurement and OFR analysis

      A single nonculprit subclinical atherosclerotic lesion was imaged for each patient (left anterior descending artery, 67.7%; left circumflex artery, 11.2%; and right coronary artery, 21.1%). The changes in nonculprit subclinical atherosclerotic lesion volume and in each component throughout the coronary segment of interest are summarized in Table 2 and Fig. 1, Fig. 2 and the central illustration.
      Table 2Nonculprit subclinical atherosclerosis measurements Using OFR.
      ParameterLR group (n = 89)LP group (n = 72)p value
      PAV
       Baseline47.50 [40.05,52.20]41.85 [35.70,47.10]<0.001
       1-year follow-up44.20 [38.50,49.45]***44.90 [40.10,51.73]***0.307
       ΔPAV−1.90 [-4.20, −0.65]3.45 [1.73,6.32]<0.001
      TAVn
       Baseline107.20 [86.0,140.83]117.70 [83.33,150.85]0.526
       1-year follow-up101.29 [82.63,127.30]***128.58 [100.95,167.17]***0.001
       ΔTAVn−6.20 [-11.81.-0.56]10.64 [7.27,18.32]<0.001
      Lipid PAV
       Baseline21.70 [14.40,28.20]16.60 [6.88,26.18]0.024
       1-year follow-up14.80 [8,75,21.15]***22.15 [10.53,28.65]***0.001
       Lipid ΔPAV−6.30 [-9.65, −2.40]3.15 [-0.10,7.33]<0.001
      Lipid TAVn
       Baseline22.91 [13.85,34.80]20.04 [6.75,31.09]0.112
       1-year follow-up13.85 [7.31,22.96]***25.80 [12.89,45.88]***<0.001
       Lipid ΔTAVn−7.18 [-12.58, −3.10]5.92 [0.98,13.63]<0.001
      Fibrous PAV
       Baseline64.20 [55.40,73.80]69.95 [55.80,76.25]0.068
       1-year follow-up73.50 [64.35,79.75]***66.75 [55.28,74.33]***0.004
       Fibrous ΔPAV6.70 [1.90,11.15]−2.25 [-5.18,0.75]<0.001
      Fibrous TAVn
       Baseline70.04 [55.46,84.56]75.97 [54.89.92.63]0.262
       1-year follow-up70.54 [58.80,89.60]78.72 [56.79,107.80]***0.091
       Fibrous ΔTAVn1.75 [-3.11,7.62]5.48 [-0.99,10.94]0.090
      Calcium PAV
       Baseline0.30 [0.10,2.50]0.35 [0.00,2.38]0.366
       1-year follow-up0.30 [0.10,3.25]0.90 [0.10,4.35]***0.299
       Calcium ΔPAV0.00 [-0.10,0.35]0.40 [0.00,1.75]0.010
      Calcium TAVn
       Baseline0.32 [0.08,2.70]0.54 [0.00,2.68]0.334
       1-year follow-up0.34 [0.06,3.37]1.33 [0.06,6.59]***0.192
       Calcium ΔTAVn0.00 [-0.18,0.28]0.70 [0.00,2.97]<0.001
      Macrophage PAV
       Baseline0.50 [0.20,0.90]0.30 [0.10,0.60]0.006
       1-year follow-up0.30 [0.10,0.60]***0.40 [0.10,0.88]*0.245
       Macrophage ΔPAV−0.10 [-0.50,0.00]0.05 [-0.10,0.28]<0.001
      Macrophage TAVn
       Baseline0.55 [0.21,1.03]0.28 [0.07,0.81]0.010
       1-year follow-up0.30 [0.10,0.61]***0.44 [0.81,1.09]**0.193
       Macrophage ΔTAVn−0.20 [-0.54,0.01]0.08 [-0.10,0.28]<0.001
      TFCT
       Baseline100.00 [59.00,127.50]110.50 [80.00,138.50]0.173
       1-year follow-up123.00 [92.00,169.00]***115.00 [75.00,159.00]0.267
       ΔTFCT25.00 [-11.00,71.50]5.00 [-17.75,49.00]0.039
      Values are expressed as the mean ± standard deviation (SD) for continuous variables with normal distribution, median (interquartile range) for continuous variables with nonnormal distribution or frequency (percentage) for categorical variables in the table. LP, lesion progression; LR, lesion regression; PAV, percent atheroma volume; TAVn, normalized total atheroma volume; TFCT, thinnest fibrous cap thickness.
      ***p < 0.001; **p < 0.01; *p < 0.05 all showed significance between the baseline and 1-year follow-up.
      Fig. 1
      Fig. 1Correlation between changes of total non-culprit atherosclerosis volume and each component in the LR group and LP group.
      Fig. 2
      Fig. 2Typical cases of nonculprit subclinical atherosclerosis regression and progression.
      Representative cross-sectional OCT image of the LR group demonstrating reduction in lipid component from baseline (lipid PAV 42.1%, (A) original OCT image, (A′) OCT image with histological-like diagram by OFR analysis to follow-up (lipid PAV 16.8%, (B) same cross-sectional OCT image with (A) by OFR analysis (B′) at follow-up). Representative cross-sectional OCT image of the LP group demonstrating increase in lipid component from baseline (lipid PAV 16.2%, (C) original OCT image without OFR analysis, (C′) OCT image with OFR analysis) to follow-up (lipid PAV 49.6%, (D) same cross-sectional OCT image with (C) by OFR analysis (D′) at follow-up). LP, lesion progression; LR, lesion regression; OCT, optical coherence tomography; OFR, optical flow ratio; PAV, percent atheroma volume.
      In the overall population in our study, no difference in TAVn (112.55 (87.04, 143.63) vs. 112.35 (84.99, 148.08), p = 0.139) or PAV (44.49 ± 8.52% vs. 44.83 ± 8.32%, p = 0.405) was found between baseline and 1-year follow-up. Notably, ΔTAVn was significantly positively correlated with lipid ΔTAVn, fibrous ΔTAVn and calcium ΔTAVn (p < 0.001); however, ΔPAV was positively correlated only with lipid ΔPAV and calcium ΔPAV (r = 0.660, p < 0.001; r = 0.189, p = 0.017) but was negatively correlated with fibrous ΔPAV (r = −0.576; p < 0.001). When we analysed the data of the LR and LP groups separately, we found a very interesting phenomenon: in the LR group, ΔTAVn was significantly positively correlated only with lipid ΔTAVn (r = 0.482, p < 0.001), no significant correlation was found between ΔTAVn and fibrous ΔTAVn (r = 0.454, p = 0.058) or calcium ΔTAVn (r = 0.053, p = 0.624). A similar positive correlation was found between ΔPAV and lipid ΔPAV (r = 0.315, p = 0.003), while ΔPAV was not correlated with either fibrous ΔPAV or calcium ΔPAV (Fig. 1(a)). In the LP group, ΔTAVn was significantly positively correlated with lipid ΔTAVn (r = 0.569, p < 0.001), fibrous ΔTAVn (r = 0.451, p < 0.001) and calcium ΔTAVn (r = 0.235, p = 0.032); however, ΔPAV was positively correlated only with lipid ΔPAV (r = 0.303, p = 0.010) and negatively correlated with fibrous ΔPAV (r = −0.308, p = 0.009). No correlation was found between ΔPAV and calcium ΔPAV (Fig. 1(b)).
      In the LR group, TAVn significantly decreased from baseline to 1-year follow-up (p < 0.001) compared with the LP group (p < 0.001). However, in the analysis of the changes in the specific composition of the plaques, only lipid TAVn decreased significantly from baseline to 1-year follow-up (p < 0.001), and no differences were found between fibrous TAVn and calcium TAVn from baseline to 1-year follow-up. Among the changes in relative values, lipid PAV decreased (p < 0.001) and fibrous PAV increased (p < 0.001) from baseline to 1-year follow-up. In the LP group, all components—lipid TAVn, fibrous TAVn and calcium TAVn—increased significantly from baseline to 1-year follow-up (p < 0.001). The changes in relative values included an increase in lipid PAV (p < 0.001) and a decrease in fibrous PAV (p < 0.001) from baseline to 1-year follow-up in contrast to those of the LR group (Table 2). Moreover, we found that TFCT increased more in the LR group (p < 0.001) than in the LP group (p > 0.05), and macrophage TAVn exhibited a decrease in the LR group (p < 0.001) and an increase in the LP group (p < 0.01).

      3.4 Exploratory analysis of the clinical factors leading to nonculprit LR

      Univariate correlation analysis revealed potential correlations of LR with nondiabetic status, use of ACEIs/ARBs, use of beta-blockers, a higher initial lipid PAV, a higher ΔLDL-C level (given its interaction effect with LDL-C, total cholesterol level was not included in the correlation analysis), and a higher ΔLDL-C%; these factors were then incorporated into multiple regression analysis to compute the standardized coefficients. Interestingly, multivariate logistic regression analysis showed that nondiabetic status, use of beta-blockers, baseline lipid PAV, and ΔLDL-C% were correlated with LR (Table 3).
      Table 3Multivariate logistic regression analysis of factors influencing plaque progression.
      VariableβS0.EWaldpOR95% CI
      lowhigh
      Diabetes1.0490.4186.3010.0122.8551.2586.475
      Beta-blocker use−1.0060.3647.6250.0060.3660.1790.747
      Baseline Lipid PAV (%)−0.0360.0174.4490.0350.9640.9320.997
      ΔLDL-C (%)1.2740.5894.6680.0313.5741.12511.347
      Constant0.8600.4204.1880.0412.363
      LDL-C, low-density lipoprotein cholesterol; PAV, percent atheroma volume.

      3.5 Exploratory analysis of MACEs related to nonculprit lesions at 3-year follow-up

      There were no MACEs related to nonculprit lesions in the two groups at 1-year follow-up. The MACEs related to nonculprit lesions at 2-year (98.9% vs. 91.7%, p = 0.026) and 3-year (97.8% vs. 90.1%, p = 0.040) follow-up was significantly lower in the LR group than in the LP group.

      4. Discussion

      The main findings in our study are as follows: 1) Regardless of the regression or progression of nonculprit subclinical atherosclerotic plaques, ΔTAVn and ΔPAV were mainly caused by changes in various components of plaques, especially lipid components, in the LR group. 2) ΔLDL-C% was the key factor predicting the outcome of nonculprit subclinical atherosclerosis. 3) There were fewer MACEs related to nonculprit lesions at 2- and 3-year follow-up in the LR group than in the LP group significantly.
      Some studies have shown that aggressive LLT results in significant regression of coronary atherosclerotic plaques as indicated by TAVn or PAV measurement based on IVUS [
      • Nissen S.E.
      • Nicholls S.J.
      • Sipahi I.
      • Libby P.
      • Raichlen J.S.
      • et al.
      Effect of very high-intensity statin therapy on regression of coronary atherosclerosis: the ASTEROID trial.
      ,
      • Nicholls S.J.
      • Ballantyne C.M.
      • Barter P.J.
      • Chapman M.J.
      • Erbel R.M.
      • et al.
      Effect of two intensive statin regimens on progression of coronary disease.
      ,
      • Tsujita K.
      • Sugiyama S.
      • Sumida H.
      • Shimomura H.
      • Yamashita T.
      • et al.
      Impact of dual lipid-lowering strategy with ezetimibe and atorvastatin on coronary plaque regression in patients with percutaneous coronary intervention: the multicenter randomized controlled PRECISE-IVUS trial.
      ,
      • Shimokado A.
      • Kubo T.
      • Nishiguchi T.
      • Katayama Y.
      • Taruya A.
      • et al.
      Automated lipid-rich plaque detection with short wavelength infra-red OCT system.
      ,
      • Li J.
      • Montarello N.J.
      • Hoogendoorn A.
      • Verjans J.W.
      • Bursill C.A.
      • et al.
      Multimodality intravascular imaging of high-risk coronary plaque, JACC Cardiovasc.
      ,
      • Kim S.
      • Nam H.S.
      • Lee M.W.
      • Kim H.J.
      • Kang W.J.
      • et al.
      Comprehensive assessment of high-risk plaques by dual-modal imaging catheter in coronary artery, JACC Basic Transl.
      ]. A previous OCT study showed that high-intensity statin therapy increased fibrous cap thickness and reduced macrophage accumulation, indicating plaque stabilization [
      • Räber L.
      • Koskinas K.C.
      • Yamaji K.
      • Taniwaki M.
      • Roffi M.
      • et al.
      Changes in coronary plaque composition in patients with acute myocardial infarction treated with high-intensity statin therapy (IBIS-4): a serial optical coherence tomography study.
      ]. A recent NIRS study showed that aggressive LLT with a PCSK9 inhibitor (PCSK9i) not only decreased TAVn and PAV but also significantly reduced the maximal lipid core burden index [
      • Ota H.
      • Omori H.
      • Kawasaki M.
      • Hirakawa A.
      • Matsuo H.
      Clinical impact of PCSK9 inhibitor on stabilization and regression of lipid-rich coronary plaques: a near-infrared spectroscopy study.
      ]. Other tissue IVUS trials based on virtual histology analysis have revealed that aggressive LLT can reduce lipid composition with or without reductions in TAVn or PAV [
      • Park S.J.
      • Kang S.J.
      • Ahn J.M.
      • Chang M.
      • Yun S.C.
      • et al.
      Effect of statin treatment on modifying plaque composition: a double-blind, randomized study.
      ,
      • Nozue T.
      • Yamamoto S.
      • Tohyama S.
      • Umezawa S.
      • Kunishima T.
      • et al.
      Statin treatment for coronary artery plaque composition based on intravascular ultrasound radiofrequency data analysis.
      ]. Although LLT is routinely used for patients with ACS in daily practice, the numbers of MACEs attributable to culprit or nonculprit lesions have been reported to be equal [
      • Lee S.E.
      • Chang H.J.
      • Sung J.M.
      • Park H.B.
      • Heo R.
      • et al.
      Effects of statins on coronary atherosclerotic plaques: the PARADIGM study.
      ]. Although many studies have demonstrated the efficacy of enhanced statin therapy, it does not mean that statin use alone can reverse plaques. A multinational observational registry study demonstrated that statins are associated with slower progression of plaque volume without affecting the stenosis severity of coronary artery lesions and induce only phenotypic plaque transformation [
      • Lee S.E.
      • Chang H.J.
      • Sung J.M.
      • Park H.B.
      • Heo R.
      • et al.
      Effects of statins on coronary atherosclerotic plaques: the PARADIGM study.
      ].
      We first used novel OFR software that can precisely analyse various plaque components to study the dynamic natural morphologies and component changes in nonculprit subclinical atherosclerosis in patients with ACS based on OCT images from the subgroup data of our previous randomized controlled trial [
      • Chu M.
      • Jia H.
      • Gutiérrez-Chico J.L.
      • Maehara A.
      • Ali Z.A.
      • et al.
      Artificial intelligence and optical coherence tomography for the automatic characterisation of human atherosclerotic plaques.
      ,
      • Zeng X.
      • Holck E.N.
      • Westra J.
      • Hu F.
      • Huang J.
      • et al.
      Impact of coronary plaque morphology on the precision of computational fractional flow reserve derived from optical coherence tomography imaging.
      ,
      • Wu X.
      • You W.
      • Wu Z.
      • Wu Q.
      • Jiang J.
      • et al.
      Ticagrelor versus clopidogrel for prevention of subclinical stent thrombosis detected by optical coherence tomography in patients with drug-eluting stent implantation-a multicenter and randomized study.
      ]. In our study, nonculprit lesions had regressed at 1-year follow-up in more than 50% of patients with ACS, even with standard LLT (statin ± ezetimibe). Our overall results revealed a positive correlation between ΔTAVn and changes in each component of the plaque; however, ΔPAV was positively correlated with lipid ΔPAV but negatively correlated with fibrous ΔPAV, which indicated that the main factors underlying the change in plaque volume were the changes in lipid components and in the relative proportions of other components. When we performed a grouping analysis based on ΔPAV, regression of nonculprit subclinical lesions in the LR group was mainly due to a decrease in lipid components and not changes in fibrous or calcified components within the plaque. However, the proportion of the lipid component decreased, that of the fibrous component increased significantly, and that of the calcium component did not change significantly when combined with TFCT, which suggests plaque stabilization. In the LP group, the progression of nonculprit plaque volume was caused by all components (lipid, fibre and calcium), especially lipid components. However, the proportions of the lipid and calcium components increased, and that of the fibrous component decreased significantly. Finally, previous studies have shown that inflammation plays an important role in the progression or regression of coronary plaque and fibrous cap thickness [
      • Räber L.
      • Koskinas K.C.
      • Yamaji K.
      • Taniwaki M.
      • Roffi M.
      • et al.
      Changes in coronary plaque composition in patients with acute myocardial infarction treated with high-intensity statin therapy (IBIS-4): a serial optical coherence tomography study.
      ,
      • Nozue T.
      • Yamamoto S.
      • Tohyama S.
      • Umezawa S.
      • Kunishima T.
      • et al.
      Statin treatment for coronary artery plaque composition based on intravascular ultrasound radiofrequency data analysis.
      ,
      • Puri R.
      • Nissen S.E.
      • Somaratne R.
      • Cho L.
      • Kastelein J.J.
      • et al.
      Impact of PCSK9 inhibition on coronary atheroma progression: rationale and design of global assessment of plaque regression with a PCSK9 antibody as measured by intravascular ultrasound (GLAGOV).
      ,
      • Yang S.
      • Koo B.K.
      • Narula J.
      Interactions between morphological plaque characteristics and coronary physiology: from pathophysiological basis to clinical implications.
      ]. Our findings confirmed that the macrophage composition was significantly decreased in the LR group and significantly increased in the LP group along with corresponding changes in TFCT, which suggests a potential correlation between plaque regression/stabilization and inflammation control. Therefore, for LLT based on statins, not only the range of relative and absolute LDL-C levels but also the effect of inflammation control should be considered. Future clinical studies should be designed to optimize the treatment of coronary de novo lesions based on intravascular and pathological imaging.
      Intensive LLT involves not only high doses of statins (±ezetimibe, PSCK9i, etc.) but also, more importantly, substantial LDL-C reduction based on medical therapy [
      • Koskinas K.C.
      • Mach F.
      • Räber L.
      Lipid-lowering therapy and percutaneous coronary interventions.
      ,
      • Barrett T.J.
      Macrophages in atherosclerosis regression.
      ,
      • Ruscica M.
      • Ferri N.
      • Santos R.D.
      • Sirtori C.R.
      • Corsini A.
      Lipid lowering drugs: present status and future developments.
      ]. Even baseline LDL-C levels were not high overall in the present study, with one third of patients already on chronic statin therapy. Our findings demonstrate that ΔLDL-C% is an independent predictive factor for changes in nonculprit subclinical atherosclerosis (p = 0.031, OR: 3.574, 95% CI: 1.125–11.347). Moreover, diabetes is an independent factor, with a 2.855-fold higher risk of plaque progression among diabetic patients than among nondiabetic patients (95% CI: 1.258–6.475). Another potential mechanism underlying the benefit of plaque regression is the initial proportion of intraplaque lipids. A previous study showed that the more lipid components there were in the plaque, the greater the benefit from intensive statin therapy via reduction of the lipid core [
      • Kini A.S.
      • Baber U.
      • Kovacic J.C.
      • Limaye A.
      • Ali Z.A.
      • et al.
      Changes in plaque lipid content after short-term intensive versus standard statin therapy: the YELLOW trial (reduction in yellow plaque by aggressive lipid-lowering therapy).
      ]. Even baseline lipid PAV was significantly higher in the LR group than in the LP group in our study. The reduction in the LDL-C level was more significant in the LR and no-change groups. This trend can be explained by the dependence of the transfer of plaques on changes in lipid composition; if there are many lipid components in the plaques, the plaque reversal benefits of enhanced LLT are likely to be greater. Beta-blocker use was also associated with LR in our analysis, but due to a lack of relevant supporting research, our interpretation of this result remains cautious; we look forward to the confirmation of this finding in future studies with larger samples.
      Previous studies have alerted us to the need to pay attention to nonculprit lesions in patients with ACS, since these lesions promote an incidence of MACEs similar to that of culprit lesions that were not treated by PCI in the previous 3 years [
      • Stone G.W.
      • Maehara A.
      • Lansky A.J.
      • De Bruyne B.
      • Cristea E.
      • et al.
      A prospective natural-history study of coronary atherosclerosis.
      ,
      • Manoharan G.
      • Ntalianis A.
      • Muller O.
      • Hamilos M.
      • Sarno G.
      • et al.
      Severity of coronary arterial stenoses responsible for acute coronary syndromes.
      ,
      • Seo Y.H.
      • Kim Y.K.
      • Song I.G.
      • Kim K.H.
      • Kwon T.G.
      • et al.
      Long-term clinical outcomes in patients with untreated non-culprit intermediate coronary lesion and evaluation of predictors by using virtual histology-intravascular ultrasound; a prospective cohort study.
      ,
      • Shimamura K.
      • Kubo T.
      • Akasaka T.
      Evaluation of coronary plaques and atherosclerosis using optical coherence tomography.
      ]. Currently, most culprit lesions in ACS patients are treated with PCI, so MACEs caused by nonculprit lesions have received increased attention. Even though there were no MACEs related to nonculprit lesions at 1-year follow-up in either group, we found that the incidence of MACEs increased significantly in the LP group at 2- and 3-year follow-up. This suggests that when LP of nonculprit subclinical atherosclerosis was found at 1-year follow-up, the current LLT did not effectively prevent plaque progression, and the consequences manifested as a subsequent increase in the incidence of MACEs that was not seen in the LR group. This finding suggests that we can predict MACEs related to nonculprit lesions by observing the dynamic natural morphology and component changes in plaques using OCT detection and OFR analysis, which will further optimize clinical LLT. If LR of nonculprit subclinical atherosclerosis is noted at 1-year follow-up, the current LLT will reduce MACEs at 3-year follow-up. If LP of nonculprit subclinical atherosclerosis occurs, more aggressive LLT (such as PCSK9i) should be used, and ΔLDL-C% is a good indicator for measuring the efficacy of LLT when the baseline LDL-C level is not high or even low.

      4.1 Clinical implications

      The prognosis of nonculprit subclinical atherosclerosis at 1-year follow-up, which can be measured by OCT and quantified using pathological analysis by novel OFR software, needs to be taken seriously because it can predict future corresponding cardiac events if the treatment plan is not changed. Whether the nonculprit subclinical atherosclerotic plaque can be reversed depends mainly on three aspects: the patient's diabetes status; pathological or pathological-like characteristics of nonculprit subclinical atherosclerosis, namely, baseline lipid PAV; and medical factors such as the effect of LLT, represented by the ΔLDL-C% reduction.

      4.2 Limitations

      This study was based on a subgroup analysis of our previous randomized controlled trial (RCT) on subclinical stent thrombosis detected by OCT, whose primary endpoint was not focused on the plaque outcome. With the development of OCT plaque analysis software, the quantitative and qualitative analysis of coronary atherosclerosis have become similar to histopathological analysis. Although the OFR software used in this study is a novel artificial intelligence framework for not only coronary physiological function but also automatic plaque characterization detection, its accuracy and robustness are needed to confirm by more studies. Admittedly, the number of patients in this study was relatively small, the baseline LDL-C level was not high compared with previous coronary plaque regression studies, LLT was not uniform, and the intensity of LLT treatment did not reach the degree necessary for LR according to previous studies.

      4.3 Conclusions

      The LR of nonculprit subclinical atherosclerosis at 1-year follow-up was mainly caused by regression of the lipid component, which was correlated with the degree of LDL-C reduction and fewer MACEs at 3-year follow-up.

      Trial registration number

      ClinicalTrials.gov. Number: NCT02140801.

      Financial support

      This work was supported by the AstraZeneca Corp of China [ISSBRIL0361] partly.

      CRediT authorship contribution statement

      Jia-cong Nong: Data curation, Formal analysis, Visualization, Writing – original draft. Wei You: Validation, Investigation. Pei-na Meng: Formal analysis, Data curation. Yi Xu: Validation. Xiang-qi Wu: Validation, Investigation. Zhi-ming Wu: Investigation. Bi-lin Tao: Data curation. Ya-jie Guo: Data curation. Song Yang: Conceptualization. De-lu Yin: Conceptualization, Validation. Fei Ye: Conceptualization, Methodology, Validation, Writing – review & editing, Supervision, Project administration.

      Declaration of competing interest

      The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

      Acknowledgements

      The authors thank Xiaoyu Huang and Ruolan Gao, MBBS, for their assistance in the determination of the corrected intracoronary images, and thank Pr. Shengxian Tu (Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University) for providing OFR software.

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