{"paper_id":"46cd8076-8ce9-4ef3-92dd-e96dc7052fa3","body_text":"The Differences in Non-Culprit Lesions Among Premenopausal, Perimenopausal, and Postmenopausal Women with Acute Coronary Syndrome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Differences in Non-Culprit Lesions Among Premenopausal, Perimenopausal, and Postmenopausal Women with Acute Coronary Syndrome Rui Sun, Chen Zhao, Luping He, Yuhan Qin, Pengyan Wu, Yue Zhu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5965842/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The features of non-culprit lesions among women in premenopausal, perimenopausal, and postmenopausal stages with acute coronary syndrome (ACS) remain unclear. Optical coherence tomography (OCT) represents a catheter-based imaging technique. This study employed OCT to investigate potential differences in non-culprit lesions among women with ACS across menopausal stages. Methods Of 194 patients with ACS who underwent OCT before the intervention, 243 non-culprit plaques were identified. Based on age, patients were categorized as premenopausal (n = 23), perimenopausal (n = 37), and postmenopausal (n = 134) cohorts, non-culprit lesion characteristics were compared across these cohorts. Results Plaque erosion exhibited higher occurrence in premenopausal women relative to those in perimenopausal and postmenopausal women (44.8% vs. 15.7% vs. 27.0%; P = 0.048). Moreover, fibrous plaques were more frequent in premenopausal women (31.0% vs. 29.4% vs. 13.5%; P = 0.040). The postmenopausal cohort showed a markedly larger mean lipid arc and lipid index in contrast to the premenopausal cohort (174.2° vs. 126.8°; P = 0.032 and 1542.7 vs. 627.8; P = 0.025, respectively). Non-culprit lesions in postmenopausal women displayed more vulnerable features, including macrophage presence (62.1% vs. 70.6% vs. 87.1%; P = 0.025), cholesterol crystals (37.9% vs. 51.0% vs. 76.7%; P = 0.001), and spotty calcification (6.9% vs. 11.8% vs. 52.1%; P = 0.001). Conclusions Postmenopausal women with ACS exhibited higher vulnerability in non-culprit lesions compared to their premenopausal and perimenopausal counterparts. Acute coronary syndrome Women Optical coherence tomography Non-culprit plaque Figures Figure 1 Figure 2 Introduction Coronary artery disease (CAD), caused by narrowing, spasms, or blockages in coronary arteries, is a significant global health issue. Despite considerable advances over the past five decades in managing cardiovascular disease (CVD) and its risk factors, much of this progress has been male-focused. Cardiovascular mortality rates among women exceed those of breast and ovarian cancers combined. 1 Epidemiological studies indicate that clinical coronary atherosclerosis onset is approximately a decade later in women relative to men, with first myocardial infarction (MI) in women occurring roughly 20 years later. The incidences of CVDs in men and women converge by age 70, with women surpassing men by age 80. Before menopause, estrogen offers cardiovascular protection; however, after menopause, atherosclerosis accelerates, ultimately reaching similar rates to that of men. 2 , 3 Research using animal atherosclerosis models has demonstrated that physiological estrogen concentrations effectively reduce the progression of lesions at initial and late phases in women, with potential protective effects observed in men. 4 However, there is limited knowledge about how non-culprit lesions or plaques differ in vivo among premenopausal, perimenopausal, and postmenopausal women with acute coronary syndrome (ACS). Optical coherence tomography (OCT), a high-resolution imaging technology with an axial resolution of roughly 10–15 µm, 5 offers markedly greater detail than intravascular ultrasonography (IVUS) for examining superficial structures of the vessel wall. 6 This investigation employed OCT to examine the characteristics of non-culprit lesions in women with ACS during premenopausal, perimenopausal, and postmenopausal phases. A more precise understanding of the relationship between reproductive aging and atherosclerosis could aid in developing targeted clinical strategies to prevent recurrent cardiovascular events and enhance cardiovascular health in women. Methods Study Population : This retrospective study analysis examined female patients with ACS (aged ≥ 18 years) admitted to the Second Affiliated Hospital of Harbin Medical University between May 2020 and June 2022. Of these patients, 248 underwent OCT to evaluate newly identified native lesions before percutaneous coronary intervention (PCI). Exclusion criteria encompassed histories of hysterectomy or oophorectomy, use of exogenous reproductive hormones, end-stage renal disease, severe hepatic dysfunction, allergies to contrast agents, contraindications to aspirin or ticagrelor, left main CAD, coronary artery bypass grafting, and cases with poor or missing OCT image quality. The final cohort consisted of 194 patients with ACS, categorized into three age-based cohorts: premenopausal (n = 23), perimenopausal (n = 37), and postmenopausal (n = 134) (Fig. 1 ). Non-culprit lesions were characterized as plaques with 30–70% angiographic diameter stenosis not treated during PCI, A non-culprit lesion identified by OCT was an untreated coronary segment with luminal narrowing and loss of the normal architecture of the vessel wall (ie, intimal, media, and adventitia). 7 The non-culprit lesions might not reside within the same vessel as the culprit lesions. identified based on stress testing or electrocardiographic (ECG) findings of spontaneous ischemic events. Comparative analysis among the cohorts included 243 non-culprit plaques from the 194 patients. This investigation was sanctioned by the Ethics Committee of the Second Affiliated Hospital of Harbin Medical University (Harbin, China). Each participant submitted signed documentation of consent. OCT Image Acquisition OCT imaging was performed on non-culprit vessels following coronary angiography and before PCI. These vessels were identified using clinical, ECG, and angiographic data. Imaging was performed with the frequency-domain OCT C7 system (OCT C7-XR Dragonfly, St. Jude Medical, Inc., St. Paul, MN). A 6- or 7-F guiding catheter supported the OCT imaging catheter advancement toward each lesion’s distal segment. An automated pullback was initiated based on clearing blood with a contrast agent or low-molecular-weight dextran injection from the guiding catheter. All acquired images underwent digital storage for offline analysis and were processed utilizing Light-Lab Image software (St. Jude Medical, Inc.) at the OCT Core Laboratory, Second Affiliated Hospital of Harbin Medical University. Definition and Classification The ST-segment elevation myocardial infarction (STEMI) manifests as persistent chest pain lasting > 30 minutes, with hospital presentation within 12 hours of initial symptoms. Diagnostic criteria include ST-segment elevation > 0.1 mV across a minimum of two contiguous leads, a new left bundle branch block on a 12-lead ECG, and heightened myocardial biomarkers (creatine kinase-muscle/brain isoform [MB] or troponin T/I). Non-ST-Elevation (NSTE)-ACS encompasses non-STEMI (NSTEMI) and unstable angina (UAP). NSTEMI exhibits ischemic symptoms without ST-segment elevation on ECG, accompanied by elevated cardiac biomarkers. Unstable angina involves novel or intensifying chest symptoms during exertion or at rest within two weeks. 8 The presence of regular menstrual cycles defines the premenopausal status. Postmenopause is characterized by the cessation of menstrual bleeding for at least 12 months or previous experiences of hysterectomy and oophorectomy. Perimenopause describes the transitional period preceding the final menstrual period, marked by increased variability in menstrual cycles, occasional amenorrhea, vasomotor dysfunction, and elevated FSH levels. 9 – 11 In OCT images, a plaque rupture (PR) is identified by a break in the fibrous cap, accompanied by cavity formation within the plaque. Plaque erosion (PE) is a thrombus on an intact, visible plaque surface without fibrous cap rupture or cavity formation. Calcification appears as a well-defined, low-signal-intensity region, while calcified nodules manifest as protrusions of calcification on the plaque surface. 12 Lipid-rich plaques are classified as those with a lipid arc > 90° and lipid length determined through longitudinal measure. The lipid index is ascertained by multiplying the mean lipid arc by lipid length. Thin-cap fibroatheromas (TCFA) are lipid plaques with a fibrous cap thickness below a defined threshold, typically with lipids occupying > 90° in circumference; a cap thickness of 65µm is commonly used as a cut-off based on histology studies. Microvessels are well-defined, hollow, low-signal-intensity structures observable across consecutive frames. Cholesterol crystals appear as thin, linear, high-intensity regions near lipid plaques. Macrophages are identified by high-signal, distinct or confluent dot-like areas with a shadow surpassing background noise. Thrombi are masses attached to or floating within the lumen, with OCT distinguishing red (erythrocyte-rich) thrombi, which show high backscattering and attenuating, and white (platelet-rich) thrombi, which have lower backscattering, uniform texture, and lower attenuation (Fig. 2 ). 13 Statistical Analysis All statistical analyses were two-tailed, with significance established as P < 0.05. Data were analyzed utilizing Statistical Package for the Social Sciences (SPSS) statistical software (version 26.0, IBM Corp., Armonk, NY, USA). The Kolmogorov–Smirnov test assessed the distribution normality of continuous variables. Data following normal distribution are denoted as mean ± standard deviation (SD), and non-normally distributed data are expressed as median (interquartile range). Group comparisons were executed utilizing analysis of variance (ANOVA) or the Kruskal–Wallis H test grounded in data distribution. Categorical variables, denoted as frequencies (%), were analyzed with the χ² or Fisher’s exact test. Bonferroni's correction for the multiple comparisons among these 3 groups was applied, thus a P value < 0.017 (0.05/3) was considered statistically significant. To address potential clustering due to multiple non-culprit plaques within the same patients, a generalized estimating equation (GEE) was employed for plaque-level comparisons of non-culprit lesion characteristics. Multivariable linear regression analysis was employed to assess the relationship between each cohort and both mean lipid angle and lipid index. The results were expressed as standardized regression coefficients (β), with the T1 group serving as the reference. The associations between each cohort and plaque erosion, fibrous plaques, macrophages, and cholesterol crystals were evaluated using multivariable logistic regression analysis. With the T1 group as the reference, the results were presented as odds ratios (OR) and 95% confidence intervals (CI). All variables with a P value < 0.1 in the univariate model were included in the multivariable model. Results Baseline Clinical Characteristics Baseline characteristics for each cohort are summarized in Table 1 . On average, the postmenopausal cohort was approximately 17 years older than the perimenopausal cohort, which was approximately 10 years older than the premenopausal cohort ( P < 0.001). Hypertension was notably more prevalent in the postmenopausal cohort in contrast to the perimenopausal and premenopausal cohorts ( P < 0.001). Among diagnoses, UAP was the most frequent in all cohorts; STEMI was more prevalent in the premenopausal cohort ( P = 0.015), NSTEMI was more common in the perimenopausal cohort ( P = 0.007), and UAP was highest in the postmenopausal cohort ( P < 0.001). The premenopausal cohort displayed superior high-density lipoprotein cholesterol (HDL-C) levels in contrast to other cohorts ( P = 0.022). Blood urea nitrogen and serum creatinine levels were elevated in the postmenopausal cohort relative to the perimenopausal and premenopausal cohorts ( P < 0.001). No significant differences were noted among the cohorts for smoking status ( P = 0.994), diabetes ( P = 0.431), family history of CAD ( P = 0.473), previous MI ( P = 0.582), total cholesterol levels in serum ( P = 0.341), triglycerides ( P = 0.846), or low-density lipoprotein (LDL) ( P = 0.149). Table 1. Baseline Clinical Characteristics. Overall (n=194) Group T1 (n=23) Group T2 (n=37) Group T3 (n=134) P value Age，years 59.8 ± 11.2 39.3 ± 5.4 49.6 ± 2.9 66.1 ± 5.8 <0.001 Smokers 43 (22.2) 5 (21.7) 8 (21.6) 30 (22.4) 0.994 Risk factors Hypertension 103 (53.1) 4 (17.4) 17 (45.9) 82 (61.2) <0.001 Diabetes mellitus 56 (28.9) 4 (17.4) 11 (29.7) 41 (30.6) 0.431 CKD 7 (3.6) 1 (4.3) 0 (0.0) 6 (4.5) 0.473 Previous MI 12 (6.2) 2 (8.7) 3 (8.1) 7 (5.2) 0.582 Presentation STEMI 40 (20.6) 10 (43.5) 7 (18.9) 23 (17.2) 0.015 NSTEMI 49 (25.3) 9 (39.1) 15 (40.5) 25 (18.7) 0.007 UAP 105 (54.1) 4 (17.4) 15 (40.5) 86 (64.2) <0.001 Laboratory data TC, mg/dL 183.9 ± 49.3 195.3 ± 66.6 188.7 ± 46.9 180.7 ± 46.4 0.341 TG, mg/dL 126.7 (91.2-187.3) 134.6 (86.4-171.8) 122.2 (91.2-174.5) 127.1 (91.2-189.3) 0.846 LDL-C, mg/dL 116.2 ± 41.4 129.5 ± 55.6 120.8 ± 40.8 112.7 ± 38.4 0.149 HDL-C, mg/dL 44.4 ± 10.4 47.9 ± 13.2 47.2 ± 10.0 43.0 ± 9.8 0.022 TC/HDL 4.3 ± 1.2 4.2 ± 1.3 4.1 ± 1.0 4.3 ± 1.3 0.539 hs-CRP, mg/L 2.0 (0.7-4.3) 3.9 (0.7-9.0) 1.5 (0.8-3.2) 1.9 (0.6-4.0) 0.510 D-dimer, ng/L 107.5 (65.8-182.2) 139.5 (79.0-196.8) 84.0 (53.0-127.5) 108.5 (70.0-182.8) 0.161 Urea, mmol/L 6.1 (5.0-7.1) 5.0 (3.9-5.8) 5.5 (4.4-6.6) 6.4 (5.5-7.3) <0.001 Crea, umol/L 66.0 (58.0-76.8) 59.0 (53.5-64.5) 63.0 (55.0-67.0) 70.0 (61.0-81.8) <0.001 Medications Aspirin 191 (98.5) 23 (100.0) 37 (100.0) 131 (97.8) 1.000 P2Y12 inhibitor 190 (97.9) 23 (100.0) 36 (97.3) 131 (97.8) 1.000 Statins 194 (100.0) 23 (100.0) 37 (100.0) 134 (100.0) NA ACEI/ARB 73 (37.6) 10 (43.5) 19 (51.4) 44 (32.8) 0.099 Values are n (%), mean ± SD, or median (IQR). A P -value < 0.05 was considered statistically significant, shown in bold . ACEI = angiotensin-converting enzyme inhibitor; ARB = angiotensin receptor blocker; CKD = chronic kidney disease; HDL-C = high-density lipoprotein cholesterol; hs-CRP = high-sensitive C-reactive protein; LDL-C = low-density lipoprotein cholesterol; MI = myocardial infarction; TC = total cholesterol; TG = triglyceride. Features of OCT Results : The OCT findings are detailed in Table 2 . PE was markedly more prevalent in the premenopausal cohort ( P = 0.048), with fibrous plaques being more common ( P = 0.040). Pairwise comparisons indicated that the postmenopausal cohort had higher mean lipid arc ( P = 0.032) and lipid index ( P = 0.025) than the premenopausal cohort (see Supplementary Table 1 for additional details). No significant differences were found in calcification angle, length, maximum arc, or minimum depth among the three cohorts. However, spotty calcification was more frequent in the postmenopausal cohort ( P = 0.001). Furthermore, the presence of macrophages ( P = 0.025) and cholesterol crystals ( P = 0.001) was notably elevated in the postmenopausal cohort relative to both perimenopausal and premenopausal cohorts. In the multivariable logistic regression analysis, perimenopausal cohort exhibited a significantly lower risk of PE (OR = 0.23, 95%CI: 0.08–0.66, P = 0.006). After adjusting for clinical risk factors, postmenopausal cohort, compared to premenopausal cohort, demonstrated a significantly higher risk of spotty calcification (OR = 18.87, 95%CI: 3.80–93.84, P < 0.001), macrophages (OR = 4.28, 95%CI: 1.64–11.20, P = 0.003), and cholesterol crystals (OR = 4.12, 95%CI: 1.52–11.18, P = 0.005) (see Supplementary Table 2A-2G for additional details). Table 2 OCT findings of nonculprit lesions. Overall (n = 243) Group T1 (n = 29) Group T2 (n = 51) Group T3 (n = 163) P value Culprit vessel 0.873 LAD 85 (35.0) 11 (37.9) 16 (31.4) 58 (35.6) LCX 80 (32.9) 9 (31.0) 17 (33.3) 54 (33.1) RCA 78 (32.1) 9 (31.0) 18 (35.3) 51 (31.3) Types of culprit plaques 0.004 Plaque rupture 16 (6.6) 1 (3.4) 2 (3.9) 13 (8.0) Plaque erosion 65 (26.7) 13 (44.8) 8 (15.7) 44 (27.0) Calcified nodules 16 (6.6) 0 (0.0) 0 (0.0) 16 (9.8) Others 146 (60.1) 15 (51.7) 41 (80.4) 90 (55.2) Lesion length, mm 13.6 (9.7–18.2) 11.4 (8.8–15.8) 13.0 (9.7–17.7) 13.6 (10.0-18.5) 0.101 Distal RLA, mm 2 5.8 (4.5–7.7) 6.0 (3.4–8.9) 5.7 (4.2–8.1) 5.8 (4.5–7.3) 0.974 Proximal RLA,mm 2 7.1 (5.2–9.5) 7.8 (4.5–10.5) 6.7 (4.9–9.1) 7.1 (5.4–9.4) 0.450 AS, % 54.5 (44.9–61.8) 51.0 (45.4–56.2) 50.7 (42.6–57.7) 54.8 (45.1–62.2) 0.134 DS, % 32.2 (25.6–38.0) 30.3 (26.0-34.1) 31.2 (24.1–36.2) 32.8 (25.8–38.8) 0.193 Meanl flow area, mm 2 4.9 (3.8–6.4) 5.9 (3.4–7.1) 4.6 (3.7–6.5) 4.9 (3.9–6.2) 0.427 Minimal flow area, mm 2 3.0 (2.2–4.1) 3.4 (2.0–5.0) 2.8 (2.2–4.2) 3.0 (2.2–3.9) 0.638 Plaque types Lipid plaque 160 (65.8) 20 (69.0) 36 (70.6) 104 (63.8) 0.696 Fibrous plaque 46 (18.9) 9 (31.0) 15 (29.4) 22 (13.5) 0.040 Calcified plaque 37 (15.2) 0 (0.0) 0 (0.0) 37 (22.7) NA LRP 141 (58.0) 16 (55.2) 31 (60.8) 94 (57.7) 0.888 Lipid length, mm 9.0 (5.8–13.0) 5.0 (2.0-10.8) 7.2 (4.7–14.2) 9.0 (6.8–13.0) 0.273 Mean lipid arc, ° 163.0 (134.3–201.0) 126.8 (112.4-171.4) 150.9 (136.5-209.8) 174.2 (137.0-194.9) 0.098 Maximum lipid arc, ° 229.8 (184.2-293.5) 178.1 (133.5–268.0) 222.5 (182.8-289.2) 243.4 (193.4-300.6) 0.310 Lipid index 1448.5 (890.6-2255.2) 627.8 (244.2-2110.9) 1149.8 (849.0-2194.4) 1542.7 (1069.5-2257.6) 0.080 Minimal FCT, µm 73.3 (60.7–86.8) 85.2 (70.2–88.5) 76.7 (70.0-83.3) 69.0 (57.5–84.0) 0.158 TCFA 47 (19.3) 3 (10.3) 7 (13.7) 37 (22.7) 0.135 Macrophage 196 (80.7) 18 (62.1) 36 (70.6) 142 (87.1) 0.025 Microvessels 73 (30.0) 7 (24.1) 9 (17.6) 57 (35.0) 0.084 Cholesterol crystals 162 (66.7) 11 (37.9) 26 (51.0) 125 (76.7) 0.001 thrombus 88 (36.2) 15 (51.7) 12 (23.5) 61 (37.4) 0.063 Thrombus length 6.7 (3.0-12.4) 3.0 (2.2-8.0) 9.3 (5.1–14.0) 7.2 (3.4–13.0) 0.241 Spotty calcific plaque 93 (38.3) 2 (6.9) 6 (11.8) 85 (52.1) 0.001 Calcium length, mm 3.6 (2.0-7.6) 3.6 (2.0-7.6) Maximal calcium arc, ◦ 172.4 (125.0-237.6) 172.4 (125.0-237.6) Minimal calcium depth, µm 10.0 (10.0–10.0) 10.0 (10.0–10.0) Values are n (%) or median (IQR). A P -value < 0.05 was considered statistically significant, shown in bold . Abbreviations as shown in Table 2 . Discussion This study revealed that PE was more prevalent in the premenopausal cohort, with fibrous plaques being the predominant non-culprit plaque type. Conversely, non-culprit plaques in the postmenopausal cohort exhibited heightened instability features through OCT assessment, including increased macrophage infiltration, cholesterol crystals, and spotty calcification relative to the premenopausal and perimenopausal cohorts. These changes appear to correlate with hormonal shifts associated with menopause. Menopause-related hormonal fluctuations adversely affect lipid metabolism, as evidenced by the higher mean lipid arc and lipid index in the postmenopausal cohort relative to the premenopausal cohort. The higher incidence of PE and fibrous plaques in the premenopausal cohort aligns with previous studies on age- and sex-related plaque composition differences in patients with ACS. These studies indicate that, unlike older women, premenopausal women exhibit plaques with more extensive cellular content and lower lipid and calcium levels, making them more susceptible to erosion rather than rupture. Moreover, younger women, compared to younger men, display a more significant proportion of fibrous tissue in non-culprit segments and lesions, and the increased presence of fibrotic plaques in non-culprit portions. 14 Low-density lipoprotein experiences oxidative transformation, generating oxidized LDL (ox-LDL), which stimulates chemokine secretion in endothelial cells and monocytes. Monocytes that migrate into the arterial wall differentiate into macrophages; estrogen selectively inhibits the expression and secretion of matrix metalloproteinase-12 (MMP12) in ox-LDL-induced macrophages (Mox macrophages). 15 This downregulation of MMP12 disrupts the cycle of macrophage aggregation. This study observed continued macrophage aggregation following menopause, likely linked to the decline in estrogen levels. The atherosclerotic process begins with macrophage infiltration, and LDL cholesterol (LDL-C) moves into the arterial wall, where LDL deposits become a primary source for cholesterol crystal formation. 16 In vitro studies indicate that cholesterol crystals develop within lipid-laden macrophage foam cells, triggering foam cell apoptosis, further macrophage recruitment, and expansion of the necrotic lipid core. Cholesterol crystal content increases with a postmenopausal decline in estrogen levels. 17 , 18 Moreover, menopause shifts calcium homeostasis, accelerating bone loss and osteoporosis, paralleling the rising prevalence and severity of calcific atherosclerosis. 19 This conclusion aligns with our study findings. Although age differences among the three cohorts were unavoidable, they did not emerge as the primary factor influencing non-culprit plaque characteristics in women. Premenopausal women exhibit a reduced incidence of CVD relative to age-matched men. 20 The markedly lower incidence of MI in premenopausal women, followed postmenopausal women by a marked increase in coronary risk, strongly suggests a pivotal function of estrogen concentrations in MI pathogenesis among women. 4 Epidemiological studies have demonstrated an inverse correlation between estrogen levels and the incidence of cardiovascular diseases, particularly in premenopausal women. However, while estrogens exhibit anti-atherogenic effects under certain conditions, they may also promote the development of atherosclerosis in other contexts. These divergent effects are likely influenced by variations in estrogen levels, receptor types, and cellular targets. The mechanisms underlying the anti-atherogenic effects of estrogens can be summarized as follows:(I) Role of Endothelial Cells: Nitric Oxide (NO) Production: Estrogens stimulate NO production in endothelial cells through both genomic and non-genomic mechanisms, promoting vasodilation. Prostacyclin (PGI2) Release: By upregulating the synthesis of COX1/2 and prostaglandin synthase, estrogens enhance PGI2 release, further facilitating vasodilation. Inhibition of Endothelial Activation: Estrogens suppress the expression of adhesion molecules such as VCAM1 and ICAM-1 on endothelial cells, reducing the adhesion and migration of monocytes and neutrophils. Enhancement of Endothelial Barrier Function: Through the modulation of tight junction proteins like occludin and claudin, estrogens strengthen the endothelial barrier, decreasing permeability to pro-atherogenic factors such as LDL. Promotion of Endothelial Cell Proliferation and Survival: Estrogens support endothelial cell proliferation and survival while inhibiting apoptosis induced by TNFα, H2O2, or oxidized LDL. (II) Role of Macrophages and Lymphocytes: Cholesterol Metabolism: Estrogens reduce cholesterol ester content in macrophages by stimulating neutral cholesterol ester hydrolase and inhibiting acyl-CoA cholesterol transferase. Modulation of Inflammatory Responses: By inhibiting NF-κB and STAT signaling pathways, estrogens decrease the production of pro-inflammatory cytokines such as TNFα and IL6 in macrophages. Macrophage Apoptosis: Estrogens promote macrophage apoptosis through the upregulation of Fas and Fas ligand, thereby reducing macrophage accumulation in atherosclerotic lesions. (III) Role of Smooth Muscle Cells: Inhibition of Cell Proliferation and Migration: Estrogens suppress the proliferation and migration of smooth muscle cells by inhibiting ERK1/2 and p38 MAPK signaling pathways. Suppression of Inflammatory Responses: Estrogens reduce the expression of pro-inflammatory cytokines like MCP1 and ET1 in smooth muscle cells, mitigating inflammation. Reduction of Oxidative Stress: By increasing the expression of antioxidant enzymes such as superoxide dismutase, estrogens alleviate oxidative stress in smooth muscle cells. (IV) Role of Estrogen Receptors (ERs): ERα and ERβ Functions: ERα plays a predominant role in the anti-atherogenic effects of estrogens, particularly in endothelial cells. ERβ also contributes by modulating inflammatory responses and apoptosis in smooth muscle cells and macrophages. GPR30 Function: GPR30, a membrane-bound estrogen receptor, is involved in regulating the proliferation and apoptosis of vascular smooth muscle cells. In summary, estrogens exert complex and context-dependent effects on atherosclerosis, with their protective mechanisms primarily mediated through endothelial cells, macrophages, smooth muscle cells, and various estrogen receptors. Further research is needed to fully elucidate the interplay between estrogen levels, receptor specificity, and cellular targets in the pathogenesis of atherosclerosis. 21 CVD continues to be the top cause of death in postmenopausal women, with menopause itself presenting an independent risk factor for CVD, separate from age. 22 Autopsy studies have indicated that men, particularly compared to premenopausal women, have a higher prevalence of necrotic cores in coronary plaques. 23 , 24 Such sex-related differences in CAD suggest a substantial protective effect of estrogen against atherosclerosis in women. Estrogen deprivation has been identified as a significant contributor to cardiovascular disease risk factors, while age appears to exert minimal influence on these established risk factors. 21 Nonetheless, age was not the primary determinant influencing the features of non-culprit plaques in menopausal women. Several intravascular ultrasound studies 7 , 14 , 25 , 26 have indicated that culprit plaques tended to be more stable in premenopausal women compared to men of the same age, while no significant differences were observed in plaque characteristics between postmenopausal women and men of the same age. Variations in coronary artery disease can frequently be attributed to the presence or absence of estrogen. 27 Consequently, differences in plaque characteristics cannot be solely attributed to age, as menopause significantly contributes to these variations. 12 Sex steroids play a critical regulatory role in modulating multiple cardiovascular risk factors implicated in CHD pathogenesis. The depletion of sex steroid hormones induces a proatherogenic lipid profile, manifested by a significant elevation in plasma LDL cholesterol concentration and triglyceride levels, coupled with a concomitant reduction in circulating HDL cholesterol. The findings of the present study are consistent with previous research findings. 28 These findings highlight that variations in plaque characteristics are not solely age-related; they are markedly influenced by menopause, especially concerning lipid and lipoprotein alterations. Histopathologic studies of coronary and aortic plaques ex vivo have demonstrated a positive correlation between plaque fibrous cap macrophage density and lipid content, and a negative correlation with fibrous cap thickness. 29 , 30 An earlier OCT investigation examining culprit lesion variations between pre- and postmenopausal women experiencing ACS indicated that culprit plaques among postmenopausal individuals exhibited heightened vulnerability features. These included TCFA, lipid-rich plaques with larger lipid arcs and lengths, macrophage accumulation, microchannels, and cholesterol crystals. These plaque characteristics were more prevalent in the postmenopausal cohort, where lipid plaques were the most common type, suggesting that such changes are directly associated with menopause. 12 This observation aligns with previous research: as estrogen levels decline after menopause, the average lipid arc and lipid index were notably elevated in postmenopausal women relative to premenopausal women. Based on longitudinal data of women’s menstrual cycles, Brambilla et al 26 and Dudley et al 31 further refined the definition of perimenopause by considering women perimenopausal if they have not had a period within the previous 3 to 11 months or if they have experienced changes in menstrual regularity (either shortening or lengthening of time between menses) during the past 12 months. In a 5-year population-based study, Brambilla et al 26 found that 3 to 11 months of amenorrhea or of irregular periods among women aged 45 to 55 years were most predictive of menopause within the following 3 years (sensitivity rate, 72% and specificity rate, 76%). Dudley et al 31 validated this definition, finding that these 2 characteristics are the best predictors of menopause 4 years after baseline (sensitivity rate, 32% and specificity rate, 99%). In this investigation, using Brambilla et al.’s definition of perimenopause as a reference standard. 32 Based on available research, this investigation the initial attempt to utilize OCT imaging to examine differences in non-culprit lesions among premenopausal, perimenopausal, and postmenopausal women with ACS. Our findings corroborate previous in vitro, autopsy, and pathology studies, enhancing our understanding of in vivo plaque characteristics across these menopausal stages. Estrogen improves lipid profiles, carbohydrate metabolism, and insulin sensitivity; it prevents the formation and progression of atherosclerotic plaques, lowers blood pressure and plasma fibrinogen levels, and positively impacts overall cardiac function. 33 Furthermore, the instability of culprit lesions was associated with lipoprotein profile alterations in postmenopausal females, indicating the possibility of tailored lipid-modifying strategies, such as PCSK-9 inhibitors, for managing acute coronary syndrome in this patient population. 12 Middle age is an optimal period for assessing women’s future CAD risk factors and implementing preventive strategies. Myocardial infarctions frequently occur in arteries with mild to moderate stenosis, as initially observed in angiography. 34 After PCI in individuals with ACS, major adverse cardiovascular events (MACE) throughout follow-up are commonly attributed to recurrent issues in culprit and non-culprit lesions. 35 This study holds valuable implications for preventing cardiovascular event recurrence, exploring the relationship between female reproductive aging and atherosclerosis, and promoting cardiovascular health in women. Study Limitations Nevertheless, this investigation faces certain limitations. First, menopausal status was broadly categorized due to limited data on physiological and psychological menopausal symptoms, using Brambilla et al.’s definition of perimenopause as a reference standard. 26 , 31 , 32 , 36 This categorization may have resulted in some overlap, as women aged 45–55 may include both premenopausal and postmenopausal individuals, introducing misclassification bias. Second, due to the lack of routine sex hormone testing in cardiology, serum estrogen or related biomarkers were not measured across cohorts. Therefore, a detailed analysis of the relationship between menopausal status and coronary atherosclerotic plaque characteristics was not conducted, warranting further research in this field. Second, due to the lack of routine sex hormone testing in cardiology, serum estrogen or related biomarkers were not measured across cohorts. Therefore, a detailed analysis of the relationship between menopausal status and coronary atherosclerotic plaque characteristics was not conducted, warranting further research in this field. Third, the relatively small sample size precluded comprehensive adjustment for potential confounding factors that might influence plaque characteristics. Fourth, the absence of stratified analyses based on conventional cardiovascular risk factors represents, potentially affecting the generalizability of our findings. Fifth, the small sample size may have limited the detection of specific significant differences, highlighting the need for more extensive, prospective studies to validate these findings. Conclusion Compared to premenopausal and perimenopausal women with ACS, postmenopausal women with ACS exhibited higher vulnerability in their non-culprit lesions. Understanding the relationship between reproductive aging and atherosclerosis is crucial for preventing recurrent cardiovascular events and supporting cardiovascular health in women. Declarations Clinical trial number: not applicable. Ethics approval and consent to participate: The original study was approved by the Ethics Committee of the Second Affiliated Hospital of Harbin Medical University (Reference number: YJSKY2023-090). Consent for publication: All patients provided written informed consent to participate. Availability of data and materials: The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors have no competing interests of interest to declare . Funding: The authors received no specific funding for this work. Authors' contributions: RS designed the study and wrote original draft. CZ, LH, and YZ participated in the OCT analysis. YQ conducted the data. PW, CS, and LC conducted the statistical analyses. LM and BY provided resources and supervised the study. All authors read and approved the final manuscript. Acknowledgements : The authors sincerely thank all colleagues and patients who participated in this study. References Palmisano BT, Zhu L, Eckel RH, Stafford JM. Sex differences in lipid and lipoprotein metabolism. Mol Metabolism. 2018;15:45–55. Qian J, Maehara A, Mintz GS, et al. Impact of gender and age on in vivo virtual histology-intravascular ultrasound imaging plaque characterization (from the global Virtual Histology Intravascular Ultrasound [VH-IVUS] registry). Am J Cardiol. 2009;103(9):1210–4. Virani SS, Alonso A, Benjamin EJ et al. Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association. Circulation 2020;141(9). Nofer JR. Estrogens and atherosclerosis: insights from animal models and cell systems. J Mol Endocrinol. 2012;48(2):R13–29. Huang D, Swanson EA, Lin CP, et al. Optical Coherence Tomography. Science. 1991;254(5035):1178–81. Jang I-K, Bouma BE, Kang D-H, et al. Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound. J Am Coll Cardiol. 2002;39(4):604–9. Pundziute G, Schuijf JD, van Velzen JE, et al. Assessment with multi-slice computed tomography and gray-scale and virtual histology intravascular ultrasound of gender-specific differences in extent and composition of coronary atherosclerotic plaques in relation to age. Am J Cardiol. 2010;105(4):480–6. AD HJFA. A, In vivo diagnosis of plaque erosion and calcified nodule in patients with acute coronary syndrome by intravascular optical coherence tomography. 2013;62(19):1748–58. Munir JA, Wu H, Bauer K, et al. The perimenopausal atherosclerosis transition. Menopause. 2012;19(1):10–5. Mangione CM, Barry MJ, Nicholson WK et al. Hormone Therapy for the Primary Prevention of Chronic Conditions in Postmenopausal Persons. JAMA 2022;328(17). Nelson HD, Menopause. Lancet. 2008;371(9614):760–70. Tang H, Li Z, Fan Y, et al. Differences in Culprit Lesions Between Premenopausal and Postmenopausal Women With Acute Coronary Syndrome: An Optical Coherence Tomography Study. Can J Cardiol. 2022;38(1):85–91. Araki M, Park SJ, Dauerman HL, et al. Optical coherence tomography in coronary atherosclerosis assessment and intervention. Nat Rev Cardiol. 2022;19(10):684–703. Ruiz-Garcia J, Lerman A, Weisz G, et al. Age- and gender-related changes in plaque composition in patients with acute coronary syndrome: the PROSPECT study. EuroIntervention. 2012;8(8):929–38. Liu SL, Bajpai A, Hawthorne EA et al. Cardiovascular protection in females linked to estrogen-dependent inhibition of arterial stiffening and macrophage MMP12. JCI Insight 2019;4(1). Abela GS. Cholesterol crystals piercing the arterial plaque and intima trigger local and systemic inflammation. J Clin Lipidol. 2010;4(3):156–64. Geng Y-J, Phillips JE, Mason RP, Casscells SW. Cholesterol crystallization and macrophage apoptosis: implication for atherosclerotic plaque instability and rupture. Biochem Pharmacol. 2003;66(8):1485–92. Kellner-Weibel G, Jerome WG, Small DM, et al. Effects of Intracellular Free Cholesterol Accumulation on Macrophage Viability. Arterioscler Thromb Vasc Biol. 1998;18(3):423–31. Stary HC, Chandler AB, Dinsmore RE, et al. A Definition of Advanced Types of Atherosclerotic Lesions and a Histological Classification of Atherosclerosis. Circulation. 1995;92(5):1355–74. Iorga A, Cunningham CM, Moazeni S, Ruffenach G, Umar S, Eghbali M. The protective role of estrogen and estrogen receptors in cardiovascular disease and the controversial use of estrogen therapy. Biology Sex Differences 2017;8(1). Senöz S, Direm B, Gülekli B, Gökmen O. Estrogen deprivation, rather than age, is responsible for the poor lipid profile and carbohydrate metabolism in women. Maturitas. 1996;25(2):107–14. Bacon JL. The Menopausal Transition. Obstet Gynecol Clin N Am. 2017;44(2):285–96. Mautner SL, Lin F, Mautner GC, Roberts WC. Comparison in women versus men of composition of atherosclerotic plaques in native coronary arteries and in saphenous veins used as aortocoronary conduits. J Am Coll Cardiol. 1993;21(6):1312–8. Dollar AL, Kragel AH, Fernicola DJ, Waclawiw MA, Roberts WC. Composition of atherosclerotic plaques in coronary arteries in women < 40 years of age with fatal coronary artery disease and implications for plaque reversibility. Am J Cardiol. 1991;67(15):1223–7. Schoenenberger AW, Urbanek N, Toggweiler S, Stuck AE, Resink TJ, Erne P. Ultrasound-assessed non-culprit and culprit coronary vessels differ by age and gender. World J Cardiol. 2013;5(3):42–8. Brambilla DJ, McKinlay SM, Johannes CB. Defining the Perimenopause for Application in Epidemiologic Investigations. Am J Epidemiol. 1994;140(12):1091–5. Feldman RD. Sex-Specific Determinants of Coronary Artery Disease and Atherosclerotic Risk Factors: Estrogen and Beyond. Can J Cardiol. 2020;36(5):706–11. Stevenson JC, Tsiligiannis S, Panay N. Cardiovascular Risk in Perimenopausal Women. Curr Vasc Pharmacol. 2019;17(6):591–4. Kolodgie FD, Burke AP, Farb A, et al. The thin-cap fibroatheroma: a type of vulnerable plaque: The major precursor lesion to acute coronary syndromes. Curr Opin Cardiol. 2001;16(5):285–92. Varnava AM, Mills PG, Davies MJ. Relationship Between Coronary Artery Remodeling and Plaque Vulnerability. Circulation. 2002;105(8):939–43. Dudley EC, Hopper JL, Taffe J, Guthrie JR, Burger HG, Dennerstein L. Using longitudinal data to define the perimenopause by menstrual cycle characteristics. Climacteric. 2009;1(1):18–25. Bastian LA, Smith CM, Nanda K. Is This Woman Perimenopausal? JAMA 2003;289(7). Rosano GMC, Chierchia SL, Leonardo F, Beale CM, Collins P. Cardioprotective effects of ovarian hormones. Eur Heart J. 1996;17(suppl D):15–9. Farb A, Burke AP, Tang AL, et al. Coronary plaque erosion without rupture into a lipid core. A frequent cause of coronary thrombosis in sudden coronary death. Circulation. 1996;93(7):1354–63. Stone GW, Maehara A, Lansky AJ, et al. A Prospective Natural-History Study of Coronary Atherosclerosis. N Engl J Med. 2011;364(3):226–35. Richard A, Rohrmann S, Mohler-Kuo M, et al. Urinary phytoestrogens and depression in perimenopausal US women: NHANES 2005–2008. J Affect Disord. 2014;156:200–5. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5965842\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":433859743,\"identity\":\"4e5cc101-0dc5-4806-bc4b-6644e399eeea\",\"order_by\":0,\"name\":\"Rui Sun\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The 2nd Affiliated Hospital of Harbin Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rui\",\"middleName\":\"\",\"lastName\":\"Sun\",\"suffix\":\"\"},{\"id\":433859749,\"identity\":\"60d7fc2a-d53d-4bd8-b0c8-4f819d84039e\",\"order_by\":1,\"name\":\"Chen Zhao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The 2nd Affiliated Hospital of Harbin Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chen\",\"middleName\":\"\",\"lastName\":\"Zhao\",\"suffix\":\"\"},{\"id\":433859750,\"identity\":\"9d4d3cf5-3f12-40c7-bdfe-666335567783\",\"order_by\":2,\"name\":\"Luping He\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The 2nd Affiliated Hospital of Harbin Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Luping\",\"middleName\":\"\",\"lastName\":\"He\",\"suffix\":\"\"},{\"id\":433859751,\"identity\":\"bdea4438-e163-4f1a-be11-309176ab40df\",\"order_by\":3,\"name\":\"Yuhan Qin\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The 2nd Affiliated Hospital of Harbin Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yuhan\",\"middleName\":\"\",\"lastName\":\"Qin\",\"suffix\":\"\"},{\"id\":433859752,\"identity\":\"593e46b6-c193-40c9-871d-8be67ccecfd3\",\"order_by\":4,\"name\":\"Pengyan Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The 2nd Affiliated Hospital of Harbin Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Pengyan\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"},{\"id\":433859753,\"identity\":\"03c78409-7892-47f3-ae76-f8ae8aaf8e82\",\"order_by\":5,\"name\":\"Yue Zhu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tianjin People’s Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yue\",\"middleName\":\"\",\"lastName\":\"Zhu\",\"suffix\":\"\"},{\"id\":433859755,\"identity\":\"2ee48026-97f7-400d-ac61-bc70dd94ff87\",\"order_by\":6,\"name\":\"Lina Cui\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The 2nd Affiliated Hospital of Harbin Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lina\",\"middleName\":\"\",\"lastName\":\"Cui\",\"suffix\":\"\"},{\"id\":433859756,\"identity\":\"640bad5b-f1aa-4de6-a538-de7e65ac377d\",\"order_by\":7,\"name\":\"Chengyu Shi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Okayama University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chengyu\",\"middleName\":\"\",\"lastName\":\"Shi\",\"suffix\":\"\"},{\"id\":433859757,\"identity\":\"1e2bbf17-e5db-4cf0-a9d1-bd81d4e17caa\",\"order_by\":8,\"name\":\"Lijia Ma\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDACdobEAxIMNsz8zMwHDnz4QYwWZoYEoJY0dsn2tsSDM3uI08JwgIHhML/BmTPGhznYiNCh28zw4IDljsPSkjNyPhxm4GGQ5xc7gF+L2WGgwyTPpBvzS+RuOFxgwWA4c3YCMVrarJMlZwC1zOBhSDC4TZwW5voNN3IeHOZhI16LMzPQ+wwkaUljBgayATCQJYjwy/GexMeSbeCofPzhww8beX5pAloYGHgSmCUQPAncChGA/QDjB2LUjYJRMApGwcgFAP9aSdnoVEUtAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"The 2nd Affiliated Hospital of Harbin Medical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Lijia\",\"middleName\":\"\",\"lastName\":\"Ma\",\"suffix\":\"\"},{\"id\":433859758,\"identity\":\"4d571652-ecf2-4c9a-8775-1d5e228ea3d4\",\"order_by\":9,\"name\":\"Bo Yu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The 2nd Affiliated Hospital of Harbin Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bo\",\"middleName\":\"\",\"lastName\":\"Yu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-02-05 12:53:21\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5965842/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5965842/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":79322793,\"identity\":\"c94cf83e-16db-43f1-aab3-868066a0bfa9\",\"added_by\":\"auto\",\"created_at\":\"2025-03-27 04:45:31\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":80438,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlowchart of the study. ACS, acute coronary syndrome; OCT, optical coherence tomography.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5965842/v1/784b0826bfcc068b9f2c813f.png\"},{\"id\":79321993,\"identity\":\"ccc5ed14-6c89-415b-893e-da5c5b240896\",\"added_by\":\"auto\",\"created_at\":\"2025-03-27 04:37:31\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":519876,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRepresentative optical coherence tomography images. \\u003cstrong\\u003ea \\u003c/strong\\u003eNormal vessel wall. \\u003cstrong\\u003eb\\u003c/strong\\u003ePlaque rupture accompanied by cavity formation (asterisk) within the plaque. \\u003cstrong\\u003ec \\u003c/strong\\u003ePlaque erosion. \\u003cstrong\\u003ed \\u003c/strong\\u003eThin-cap fibroatheroma. \\u003cstrong\\u003ef\\u003c/strong\\u003e White thrombus. \\u003cstrong\\u003eg\\u003c/strong\\u003eRed thrombus. \\u003cstrong\\u003eh\\u003c/strong\\u003e Macrophages. \\u003cstrong\\u003ei \\u003c/strong\\u003eMicrovessels. \\u003cstrong\\u003ej \\u003c/strong\\u003eCholesterol crystals.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5965842/v1/40935721fc6690c6944b32d4.png\"},{\"id\":79602818,\"identity\":\"9c1af9eb-9b57-4a52-842a-ef1563f623a0\",\"added_by\":\"auto\",\"created_at\":\"2025-03-31 15:31:48\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1480391,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5965842/v1/cd126ecc-de20-4018-a7f1-2382227fcd1c.pdf\"},{\"id\":79321990,\"identity\":\"33685067-247b-4b54-b7f9-8c8647f0b2bc\",\"added_by\":\"auto\",\"created_at\":\"2025-03-27 04:37:31\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":55420,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryFile.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5965842/v1/838ad093081f4f5fb58345d4.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"The Differences in Non-Culprit Lesions Among Premenopausal, Perimenopausal, and Postmenopausal Women with Acute Coronary Syndrome\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eCoronary artery disease (CAD), caused by narrowing, spasms, or blockages in coronary arteries, is a significant global health issue. Despite considerable advances over the past five decades in managing cardiovascular disease (CVD) and its risk factors, much of this progress has been male-focused. Cardiovascular mortality rates among women exceed those of breast and ovarian cancers combined.\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e Epidemiological studies indicate that clinical coronary atherosclerosis onset is approximately a decade later in women relative to men, with first myocardial infarction (MI) in women occurring roughly 20 years later. The incidences of CVDs in men and women converge by age 70, with women surpassing men by age 80. Before menopause, estrogen offers cardiovascular protection; however, after menopause, atherosclerosis accelerates, ultimately reaching similar rates to that of men.\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e Research using animal atherosclerosis models has demonstrated that physiological estrogen concentrations effectively reduce the progression of lesions at initial and late phases in women, with potential protective effects observed in men.\\u003csup\\u003e\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e However, there is limited knowledge about how non-culprit lesions or plaques differ in vivo among premenopausal, perimenopausal, and postmenopausal women with acute coronary syndrome (ACS). Optical coherence tomography (OCT), a high-resolution imaging technology with an axial resolution of roughly 10\\u0026ndash;15 \\u0026micro;m,\\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e offers markedly greater detail than intravascular ultrasonography (IVUS) for examining superficial structures of the vessel wall.\\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e This investigation employed OCT to examine the characteristics of non-culprit lesions in women with ACS during premenopausal, perimenopausal, and postmenopausal phases. A more precise understanding of the relationship between reproductive aging and atherosclerosis could aid in developing targeted clinical strategies to prevent recurrent cardiovascular events and enhance cardiovascular health in women.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e\\u003cb\\u003eStudy Population\\u003c/b\\u003e: This retrospective study analysis examined female patients with ACS (aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;18 years) admitted to the Second Affiliated Hospital of Harbin Medical University between May 2020 and June 2022. Of these patients, 248 underwent OCT to evaluate newly identified native lesions before percutaneous coronary intervention (PCI). Exclusion criteria encompassed histories of hysterectomy or oophorectomy, use of exogenous reproductive hormones, end-stage renal disease, severe hepatic dysfunction, allergies to contrast agents, contraindications to aspirin or ticagrelor, left main CAD, coronary artery bypass grafting, and cases with poor or missing OCT image quality. The final cohort consisted of 194 patients with ACS, categorized into three age-based cohorts: premenopausal (n\\u0026thinsp;=\\u0026thinsp;23), perimenopausal (n\\u0026thinsp;=\\u0026thinsp;37), and postmenopausal (n\\u0026thinsp;=\\u0026thinsp;134) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Non-culprit lesions were characterized as plaques with 30\\u0026ndash;70% angiographic diameter stenosis not treated during PCI, A non-culprit lesion identified by OCT was an untreated coronary segment with luminal narrowing and loss of the normal architecture of the vessel wall (ie, intimal, media, and adventitia).\\u003csup\\u003e\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e The non-culprit lesions might not reside within the same vessel as the culprit lesions. identified based on stress testing or electrocardiographic (ECG) findings of spontaneous ischemic events. Comparative analysis among the cohorts included 243 non-culprit plaques from the 194 patients. This investigation was sanctioned by the Ethics Committee of the Second Affiliated Hospital of Harbin Medical University (Harbin, China). Each participant submitted signed documentation of consent.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eOCT Image Acquisition\\u003c/strong\\u003e \\u003cp\\u003eOCT imaging was performed on non-culprit vessels following coronary angiography and before PCI. These vessels were identified using clinical, ECG, and angiographic data. Imaging was performed with the frequency-domain OCT C7 system (OCT C7-XR Dragonfly, St. Jude Medical, Inc., St. Paul, MN). A 6- or 7-F guiding catheter supported the OCT imaging catheter advancement toward each lesion\\u0026rsquo;s distal segment. An automated pullback was initiated based on clearing blood with a contrast agent or low-molecular-weight dextran injection from the guiding catheter. All acquired images underwent digital storage for offline analysis and were processed utilizing Light-Lab Image software (St. Jude Medical, Inc.) at the OCT Core Laboratory, Second Affiliated Hospital of Harbin Medical University.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eDefinition and Classification\\u003c/strong\\u003e \\u003cp\\u003eThe ST-segment elevation myocardial infarction (STEMI) manifests as persistent chest pain lasting\\u0026thinsp;\\u0026gt;\\u0026thinsp;30 minutes, with hospital presentation within 12 hours of initial symptoms. Diagnostic criteria include ST-segment elevation\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.1 mV across a minimum of two contiguous leads, a new left bundle branch block on a 12-lead ECG, and heightened myocardial biomarkers (creatine kinase-muscle/brain isoform [MB] or troponin T/I). Non-ST-Elevation (NSTE)-ACS encompasses non-STEMI (NSTEMI) and unstable angina (UAP). NSTEMI exhibits ischemic symptoms without ST-segment elevation on ECG, accompanied by elevated cardiac biomarkers. Unstable angina involves novel or intensifying chest symptoms during exertion or at rest within two weeks.\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e The presence of regular menstrual cycles defines the premenopausal status. Postmenopause is characterized by the cessation of menstrual bleeding for at least 12 months or previous experiences of hysterectomy and oophorectomy. Perimenopause describes the transitional period preceding the final menstrual period, marked by increased variability in menstrual cycles, occasional amenorrhea, vasomotor dysfunction, and elevated FSH levels.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR10\\\" citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e In OCT images, a plaque rupture (PR) is identified by a break in the fibrous cap, accompanied by cavity formation within the plaque. Plaque erosion (PE) is a thrombus on an intact, visible plaque surface without fibrous cap rupture or cavity formation. Calcification appears as a well-defined, low-signal-intensity region, while calcified nodules manifest as protrusions of calcification on the plaque surface.\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e Lipid-rich plaques are classified as those with a lipid arc\\u0026thinsp;\\u0026gt;\\u0026thinsp;90\\u0026deg; and lipid length determined through longitudinal measure. The lipid index is ascertained by multiplying the mean lipid arc by lipid length. Thin-cap fibroatheromas (TCFA) are lipid plaques with a fibrous cap thickness below a defined threshold, typically with lipids occupying\\u0026thinsp;\\u0026gt;\\u0026thinsp;90\\u0026deg; in circumference; a cap thickness of 65\\u0026micro;m is commonly used as a cut-off based on histology studies. Microvessels are well-defined, hollow, low-signal-intensity structures observable across consecutive frames. Cholesterol crystals appear as thin, linear, high-intensity regions near lipid plaques. Macrophages are identified by high-signal, distinct or confluent dot-like areas with a shadow surpassing background noise. Thrombi are masses attached to or floating within the lumen, with OCT distinguishing red (erythrocyte-rich) thrombi, which show high backscattering and attenuating, and white (platelet-rich) thrombi, which have lower backscattering, uniform texture, and lower attenuation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eStatistical Analysis\\u003c/strong\\u003e \\u003cp\\u003eAll statistical analyses were two-tailed, with significance established as P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. Data were analyzed utilizing Statistical Package for the Social Sciences (SPSS) statistical software (version 26.0, IBM Corp., Armonk, NY, USA). The Kolmogorov\\u0026ndash;Smirnov test assessed the distribution normality of continuous variables. Data following normal distribution are denoted as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation (SD), and non-normally distributed data are expressed as median (interquartile range). Group comparisons were executed utilizing analysis of variance (ANOVA) or the Kruskal\\u0026ndash;Wallis H test grounded in data distribution. Categorical variables, denoted as frequencies (%), were analyzed with the χ\\u0026sup2; or Fisher\\u0026rsquo;s exact test. Bonferroni's correction for the multiple comparisons among these 3 groups was applied, thus a P value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.017 (0.05/3) was considered statistically significant. To address potential clustering due to multiple non-culprit plaques within the same patients, a generalized estimating equation (GEE) was employed for plaque-level comparisons of non-culprit lesion characteristics.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003eMultivariable linear regression analysis was employed to assess the relationship between each cohort and both mean lipid angle and lipid index. The results were expressed as standardized regression coefficients (β), with the T1 group serving as the reference. The associations between each cohort and plaque erosion, fibrous plaques, macrophages, and cholesterol crystals were evaluated using multivariable logistic regression analysis. With the T1 group as the reference, the results were presented as odds ratios (OR) and 95% confidence intervals (CI). All variables with a P value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.1 in the univariate model were included in the multivariable model.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e \\u003cstrong\\u003eBaseline Clinical Characteristics\\u003c/strong\\u003e \\u003cp\\u003eBaseline characteristics for each cohort are summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. On average, the postmenopausal cohort was approximately 17 years older than the perimenopausal cohort, which was approximately 10 years older than the premenopausal cohort (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Hypertension was notably more prevalent in the postmenopausal cohort in contrast to the perimenopausal and premenopausal cohorts (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Among diagnoses, UAP was the most frequent in all cohorts; STEMI was more prevalent in the premenopausal cohort (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.015), NSTEMI was more common in the perimenopausal cohort (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.007), and UAP was highest in the postmenopausal cohort (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). The premenopausal cohort displayed superior high-density lipoprotein cholesterol (HDL-C) levels in contrast to other cohorts (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.022). Blood urea nitrogen and serum creatinine levels were elevated in the postmenopausal cohort relative to the perimenopausal and premenopausal cohorts (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). No significant differences were noted among the cohorts for smoking status (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.994), diabetes (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.431), family history of CAD (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.473), previous MI (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.582), total cholesterol levels in serum (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.341), triglycerides (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.846), or low-density lipoprotein (LDL) (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.149).\\u003c/p\\u003e \\u003cp\\u003e\\u003cstrong\\u003eTable 1. Baseline Clinical Characteristics.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"573\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eOverall\\u003c/p\\u003e\\n \\u003cp\\u003e(n=194)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eGroup T1\\u003c/p\\u003e\\n \\u003cp\\u003e(n=23)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eGroup T2\\u003c/p\\u003e\\n \\u003cp\\u003e(n=37)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eGroup T3\\u003c/p\\u003e\\n \\u003cp\\u003e(n=134)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003eAge，years\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e59.8 \\u0026plusmn; 11.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e39.3 \\u0026plusmn; 5.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e49.6 \\u0026plusmn; 2.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e66.1 \\u0026plusmn; 5.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003eSmokers\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e43 (22.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e5 (21.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e8 (21.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e30 (22.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e0.994\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eRisk factors\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Hypertension\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e103 (53.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e4 (17.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e17 (45.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e82 (61.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Diabetes mellitus\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e56 (28.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e4 (17.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e11 (29.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e41 (30.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e0.431\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;CKD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e7 (3.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e1 (4.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e0 (0.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e6 (4.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e0.473\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003ePrevious MI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e12 (6.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e2 (8.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e3 (8.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e7 (5.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e0.582\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePresentation\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;STEMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e40 (20.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e10 (43.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e7 (18.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e23 (17.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.015\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;NSTEMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e49 (25.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e9 (39.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e15 (40.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e25 (18.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.007\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;UAP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e105 (54.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e4 (17.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e15 (40.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e86 (64.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLaboratory data\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;TC, mg/dL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e183.9 \\u0026plusmn; 49.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e195.3 \\u0026plusmn; 66.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e188.7 \\u0026plusmn; 46.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e180.7 \\u0026plusmn; 46.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e0.341\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;TG, mg/dL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e126.7 (91.2-187.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e134.6 (86.4-171.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e122.2 (91.2-174.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e127.1 (91.2-189.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e0.846\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eLDL-C, mg/dL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e116.2 \\u0026plusmn; 41.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e129.5 \\u0026plusmn; 55.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e120.8 \\u0026plusmn; 40.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e112.7 \\u0026plusmn; 38.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e0.149\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;HDL-C, mg/dL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e44.4 \\u0026plusmn; 10.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e47.9 \\u0026plusmn; 13.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e47.2 \\u0026plusmn; 10.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e43.0 \\u0026plusmn; 9.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.022\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;TC/HDL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e4.3 \\u0026plusmn; 1.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e4.2 \\u0026plusmn; 1.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e4.1 \\u0026plusmn; 1.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e4.3 \\u0026plusmn; 1.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e0.539\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;hs-CRP, mg/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e2.0 (0.7-4.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e3.9 (0.7-9.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e1.5 (0.8-3.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e1.9 (0.6-4.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e0.510\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;D-dimer, ng/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e107.5 (65.8-182.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e139.5 (79.0-196.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e84.0 (53.0-127.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e108.5 (70.0-182.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e0.161\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Urea, mmol/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e6.1 (5.0-7.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e5.0 (3.9-5.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e5.5 (4.4-6.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e6.4 (5.5-7.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Crea, umol/L\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e66.0 (58.0-76.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e59.0 (53.5-64.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e63.0 (55.0-67.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e70.0 (61.0-81.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMedications\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Aspirin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e191 (98.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e23 (100.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e37 (100.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e131 (97.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;P2Y12 inhibitor\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e190 (97.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e23 (100.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e36 (97.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e131 (97.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Statins\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e194 (100.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e23 (100.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e37 (100.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e134 (100.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 114px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;ACEI/ARB\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e73 (37.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e10 (43.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e19 (51.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e44 (32.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e0.099\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eValues are n (%), mean \\u0026plusmn; SD, or median (IQR). A \\u003cem\\u003eP\\u003c/em\\u003e-value \\u0026lt; 0.05 was considered statistically significant, shown in \\u003cstrong\\u003ebold\\u003c/strong\\u003e. ACEI = angiotensin-converting enzyme inhibitor; ARB = angiotensin receptor blocker; CKD = chronic kidney disease; HDL-C = high-density lipoprotein cholesterol; hs-CRP = high-sensitive C-reactive protein; LDL-C = low-density lipoprotein cholesterol; MI = myocardial infarction; TC = total cholesterol; TG = triglyceride.\\u003c/p\\u003e\\u003cp\\u003e \\u003cb\\u003eFeatures of OCT Results\\u003c/b\\u003e: The OCT findings are detailed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. PE was markedly more prevalent in the premenopausal cohort (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.048), with fibrous plaques being more common (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.040). Pairwise comparisons indicated that the postmenopausal cohort had higher mean lipid arc (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.032) and lipid index (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.025) than the premenopausal cohort (see Supplementary Table\\u0026nbsp;1 for additional details). No significant differences were found in calcification angle, length, maximum arc, or minimum depth among the three cohorts. However, spotty calcification was more frequent in the postmenopausal cohort (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.001). Furthermore, the presence of macrophages (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.025) and cholesterol crystals (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.001) was notably elevated in the postmenopausal cohort relative to both perimenopausal and premenopausal cohorts. In the multivariable logistic regression analysis, perimenopausal cohort exhibited a significantly lower risk of PE (OR\\u0026thinsp;=\\u0026thinsp;0.23, 95%CI: 0.08\\u0026ndash;0.66, P\\u0026thinsp;=\\u0026thinsp;0.006). After adjusting for clinical risk factors, postmenopausal cohort, compared to premenopausal cohort, demonstrated a significantly higher risk of spotty calcification (OR\\u0026thinsp;=\\u0026thinsp;18.87, 95%CI: 3.80\\u0026ndash;93.84, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), macrophages (OR\\u0026thinsp;=\\u0026thinsp;4.28, 95%CI: 1.64\\u0026ndash;11.20, P\\u0026thinsp;=\\u0026thinsp;0.003), and cholesterol crystals (OR\\u0026thinsp;=\\u0026thinsp;4.12, 95%CI: 1.52\\u0026ndash;11.18, P\\u0026thinsp;=\\u0026thinsp;0.005) (see Supplementary Table\\u0026nbsp;2A-2G for additional details).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eOCT findings of nonculprit lesions.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOverall\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;243)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGroup T1\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;29)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eGroup T2\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;51)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eGroup T3\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;163)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCulprit vessel\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.873\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLAD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e85 (35.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11 (37.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16 (31.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e58 (35.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLCX\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e80 (32.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 (31.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17 (33.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e54 (33.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRCA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e78 (32.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 (31.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18 (35.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e51 (31.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTypes of culprit plaques\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.004\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlaque rupture\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16 (6.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 (3.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 (3.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13 (8.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlaque erosion\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e65 (26.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13 (44.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8 (15.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e44 (27.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCalcified nodules\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16 (6.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e16 (9.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOthers\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e146 (60.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (51.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e41 (80.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e90 (55.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLesion length, mm\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13.6 (9.7\\u0026ndash;18.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.4 (8.8\\u0026ndash;15.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13.0 (9.7\\u0026ndash;17.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13.6 (10.0-18.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.101\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistal RLA, mm\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.8 (4.5\\u0026ndash;7.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.0 (3.4\\u0026ndash;8.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.7 (4.2\\u0026ndash;8.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.8 (4.5\\u0026ndash;7.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.974\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eProximal RLA,mm\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.1 (5.2\\u0026ndash;9.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.8 (4.5\\u0026ndash;10.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.7 (4.9\\u0026ndash;9.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.1 (5.4\\u0026ndash;9.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.450\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAS, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e54.5 (44.9\\u0026ndash;61.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51.0 (45.4\\u0026ndash;56.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e50.7 (42.6\\u0026ndash;57.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e54.8 (45.1\\u0026ndash;62.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.134\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDS, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e32.2 (25.6\\u0026ndash;38.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30.3 (26.0-34.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e31.2 (24.1\\u0026ndash;36.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e32.8 (25.8\\u0026ndash;38.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.193\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMeanl flow area, mm\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.9 (3.8\\u0026ndash;6.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.9 (3.4\\u0026ndash;7.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.6 (3.7\\u0026ndash;6.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.9 (3.9\\u0026ndash;6.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.427\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMinimal flow area, mm\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.0 (2.2\\u0026ndash;4.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.4 (2.0\\u0026ndash;5.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.8 (2.2\\u0026ndash;4.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.0 (2.2\\u0026ndash;3.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.638\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePlaque types\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLipid plaque\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e160 (65.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20 (69.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e36 (70.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e104 (63.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.696\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFibrous plaque\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e46 (18.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 (31.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15 (29.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e22 (13.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.040\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCalcified plaque\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e37 (15.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e37 (22.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eNA\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLRP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e141 (58.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16 (55.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e31 (60.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e94 (57.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.888\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLipid length, mm\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.0 (5.8\\u0026ndash;13.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.0 (2.0-10.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.2 (4.7\\u0026ndash;14.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9.0 (6.8\\u0026ndash;13.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.273\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMean lipid arc, \\u0026deg;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e163.0 (134.3\\u0026ndash;201.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e126.8 (112.4-171.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e150.9 (136.5-209.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e174.2 (137.0-194.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.098\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMaximum lipid arc, \\u0026deg;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e229.8 (184.2-293.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e178.1 (133.5\\u0026ndash;268.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e222.5 (182.8-289.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e243.4 (193.4-300.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.310\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLipid index\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1448.5 (890.6-2255.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e627.8 (244.2-2110.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1149.8 (849.0-2194.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1542.7 (1069.5-2257.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.080\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMinimal FCT, \\u0026micro;m\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e73.3 (60.7\\u0026ndash;86.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e85.2 (70.2\\u0026ndash;88.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e76.7 (70.0-83.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e69.0 (57.5\\u0026ndash;84.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.158\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTCFA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e47 (19.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (10.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (13.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e37 (22.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.135\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMacrophage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e196 (80.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18 (62.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e36 (70.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e142 (87.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.025\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMicrovessels\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e73 (30.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7 (24.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9 (17.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e57 (35.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.084\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCholesterol crystals\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e162 (66.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11 (37.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e26 (51.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e125 (76.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ethrombus\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e88 (36.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (51.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12 (23.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e61 (37.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.063\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eThrombus length\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.7 (3.0-12.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.0 (2.2-8.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.3 (5.1\\u0026ndash;14.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.2 (3.4\\u0026ndash;13.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.241\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSpotty calcific plaque\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e93 (38.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 (6.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (11.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e85 (52.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCalcium length, mm\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.6 (2.0-7.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.6 (2.0-7.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMaximal calcium arc, ◦\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e172.4 (125.0-237.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e172.4 (125.0-237.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMinimal calcium depth, \\u0026micro;m\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10.0 (10.0\\u0026ndash;10.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10.0 (10.0\\u0026ndash;10.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eValues are n (%) or median (IQR). A \\u003cem\\u003eP\\u003c/em\\u003e-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered statistically significant, shown in \\u003cb\\u003ebold\\u003c/b\\u003e. Abbreviations as shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study revealed that PE was more prevalent in the premenopausal cohort, with fibrous plaques being the predominant non-culprit plaque type. Conversely, non-culprit plaques in the postmenopausal cohort exhibited heightened instability features through OCT assessment, including increased macrophage infiltration, cholesterol crystals, and spotty calcification relative to the premenopausal and perimenopausal cohorts. These changes appear to correlate with hormonal shifts associated with menopause. Menopause-related hormonal fluctuations adversely affect lipid metabolism, as evidenced by the higher mean lipid arc and lipid index in the postmenopausal cohort relative to the premenopausal cohort.\\u003c/p\\u003e \\u003cp\\u003eThe higher incidence of PE and fibrous plaques in the premenopausal cohort aligns with previous studies on age- and sex-related plaque composition differences in patients with ACS. These studies indicate that, unlike older women, premenopausal women exhibit plaques with more extensive cellular content and lower lipid and calcium levels, making them more susceptible to erosion rather than rupture. Moreover, younger women, compared to younger men, display a more significant proportion of fibrous tissue in non-culprit segments and lesions, and the increased presence of fibrotic plaques in non-culprit portions.\\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e Low-density lipoprotein experiences oxidative transformation, generating oxidized LDL (ox-LDL), which stimulates chemokine secretion in endothelial cells and monocytes. Monocytes that migrate into the arterial wall differentiate into macrophages; estrogen selectively inhibits the expression and secretion of matrix metalloproteinase-12 (MMP12) in ox-LDL-induced macrophages (Mox macrophages).\\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e This downregulation of MMP12 disrupts the cycle of macrophage aggregation. This study observed continued macrophage aggregation following menopause, likely linked to the decline in estrogen levels. The atherosclerotic process begins with macrophage infiltration, and LDL cholesterol (LDL-C) moves into the arterial wall, where LDL deposits become a primary source for cholesterol crystal formation.\\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e In vitro studies indicate that cholesterol crystals develop within lipid-laden macrophage foam cells, triggering foam cell apoptosis, further macrophage recruitment, and expansion of the necrotic lipid core. Cholesterol crystal content increases with a postmenopausal decline in estrogen levels.\\u003csup\\u003e\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e Moreover, menopause shifts calcium homeostasis, accelerating bone loss and osteoporosis, paralleling the rising prevalence and severity of calcific atherosclerosis.\\u003csup\\u003e\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u003c/sup\\u003e This conclusion aligns with our study findings. Although age differences among the three cohorts were unavoidable, they did not emerge as the primary factor influencing non-culprit plaque characteristics in women. Premenopausal women exhibit a reduced incidence of CVD relative to age-matched men.\\u003csup\\u003e\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e The markedly lower incidence of MI in premenopausal women, followed postmenopausal women by a marked increase in coronary risk, strongly suggests a pivotal function of estrogen concentrations in MI pathogenesis among women.\\u003csup\\u003e\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e Epidemiological studies have demonstrated an inverse correlation between estrogen levels and the incidence of cardiovascular diseases, particularly in premenopausal women. However, while estrogens exhibit anti-atherogenic effects under certain conditions, they may also promote the development of atherosclerosis in other contexts. These divergent effects are likely influenced by variations in estrogen levels, receptor types, and cellular targets. The mechanisms underlying the anti-atherogenic effects of estrogens can be summarized as follows:(I) Role of Endothelial Cells: Nitric Oxide (NO) Production: Estrogens stimulate NO production in endothelial cells through both genomic and non-genomic mechanisms, promoting vasodilation. Prostacyclin (PGI2) Release: By upregulating the synthesis of COX1/2 and prostaglandin synthase, estrogens enhance PGI2 release, further facilitating vasodilation. Inhibition of Endothelial Activation: Estrogens suppress the expression of adhesion molecules such as VCAM1 and ICAM-1 on endothelial cells, reducing the adhesion and migration of monocytes and neutrophils. Enhancement of Endothelial Barrier Function: Through the modulation of tight junction proteins like occludin and claudin, estrogens strengthen the endothelial barrier, decreasing permeability to pro-atherogenic factors such as LDL. Promotion of Endothelial Cell Proliferation and Survival: Estrogens support endothelial cell proliferation and survival while inhibiting apoptosis induced by TNFα, H2O2, or oxidized LDL. (II) Role of Macrophages and Lymphocytes: Cholesterol Metabolism: Estrogens reduce cholesterol ester content in macrophages by stimulating neutral cholesterol ester hydrolase and inhibiting acyl-CoA cholesterol transferase. Modulation of Inflammatory Responses: By inhibiting NF-κB and STAT signaling pathways, estrogens decrease the production of pro-inflammatory cytokines such as TNFα and IL6 in macrophages. Macrophage Apoptosis: Estrogens promote macrophage apoptosis through the upregulation of Fas and Fas ligand, thereby reducing macrophage accumulation in atherosclerotic lesions. (III) Role of Smooth Muscle Cells: Inhibition of Cell Proliferation and Migration: Estrogens suppress the proliferation and migration of smooth muscle cells by inhibiting ERK1/2 and p38 MAPK signaling pathways. Suppression of Inflammatory Responses: Estrogens reduce the expression of pro-inflammatory cytokines like MCP1 and ET1 in smooth muscle cells, mitigating inflammation. Reduction of Oxidative Stress: By increasing the expression of antioxidant enzymes such as superoxide dismutase, estrogens alleviate oxidative stress in smooth muscle cells. (IV) Role of Estrogen Receptors (ERs): ERα and ERβ Functions: ERα plays a predominant role in the anti-atherogenic effects of estrogens, particularly in endothelial cells. ERβ also contributes by modulating inflammatory responses and apoptosis in smooth muscle cells and macrophages. GPR30 Function: GPR30, a membrane-bound estrogen receptor, is involved in regulating the proliferation and apoptosis of vascular smooth muscle cells. In summary, estrogens exert complex and context-dependent effects on atherosclerosis, with their protective mechanisms primarily mediated through endothelial cells, macrophages, smooth muscle cells, and various estrogen receptors. Further research is needed to fully elucidate the interplay between estrogen levels, receptor specificity, and cellular targets in the pathogenesis of atherosclerosis.\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e CVD continues to be the top cause of death in postmenopausal women, with menopause itself presenting an independent risk factor for CVD, separate from age.\\u003csup\\u003e\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e Autopsy studies have indicated that men, particularly compared to premenopausal women, have a higher prevalence of necrotic cores in coronary plaques.\\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003e Such sex-related differences in CAD suggest a substantial protective effect of estrogen against atherosclerosis in women. Estrogen deprivation has been identified as a significant contributor to cardiovascular disease risk factors, while age appears to exert minimal influence on these established risk factors.\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e Nonetheless, age was not the primary determinant influencing the features of non-culprit plaques in menopausal women. Several intravascular ultrasound studies\\u003csup\\u003e\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e have indicated that culprit plaques tended to be more stable in premenopausal women compared to men of the same age, while no significant differences were observed in plaque characteristics between postmenopausal women and men of the same age. Variations in coronary artery disease can frequently be attributed to the presence or absence of estrogen.\\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e Consequently, differences in plaque characteristics cannot be solely attributed to age, as menopause significantly contributes to these variations.\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e Sex steroids play a critical regulatory role in modulating multiple cardiovascular risk factors implicated in CHD pathogenesis. The depletion of sex steroid hormones induces a proatherogenic lipid profile, manifested by a significant elevation in plasma LDL cholesterol concentration and triglyceride levels, coupled with a concomitant reduction in circulating HDL cholesterol. The findings of the present study are consistent with previous research findings.\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e These findings highlight that variations in plaque characteristics are not solely age-related; they are markedly influenced by menopause, especially concerning lipid and lipoprotein alterations. Histopathologic studies of coronary and aortic plaques ex vivo have demonstrated a positive correlation between plaque fibrous cap macrophage density and lipid content, and a negative correlation with fibrous cap thickness.\\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e An earlier OCT investigation examining culprit lesion variations between pre- and postmenopausal women experiencing ACS indicated that culprit plaques among postmenopausal individuals exhibited heightened vulnerability features. These included TCFA, lipid-rich plaques with larger lipid arcs and lengths, macrophage accumulation, microchannels, and cholesterol crystals. These plaque characteristics were more prevalent in the postmenopausal cohort, where lipid plaques were the most common type, suggesting that such changes are directly associated with menopause.\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e This observation aligns with previous research: as estrogen levels decline after menopause, the average lipid arc and lipid index were notably elevated in postmenopausal women relative to premenopausal women.\\u003c/p\\u003e \\u003cp\\u003eBased on longitudinal data of women\\u0026rsquo;s menstrual cycles, Brambilla et al\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e and Dudley et al\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e further refined the definition of perimenopause by considering women perimenopausal if they have not had a period within the previous 3 to 11 months or if they have experienced changes in menstrual regularity (either shortening or lengthening of time between menses) during the past 12 months. In a 5-year population-based study, Brambilla et al\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e found that 3 to 11 months of amenorrhea or of irregular periods among women aged 45 to 55 years were most predictive of menopause within the following 3 years (sensitivity rate, 72% and specificity rate, 76%). Dudley et al\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e validated this definition, finding that these 2 characteristics are the best predictors of menopause 4 years after baseline (sensitivity rate, 32% and specificity rate, 99%). In this investigation, using Brambilla et al.\\u0026rsquo;s definition of perimenopause as a reference standard.\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e Based on available research, this investigation the initial attempt to utilize OCT imaging to examine differences in non-culprit lesions among premenopausal, perimenopausal, and postmenopausal women with ACS. Our findings corroborate previous in vitro, autopsy, and pathology studies, enhancing our understanding of in vivo plaque characteristics across these menopausal stages. Estrogen improves lipid profiles, carbohydrate metabolism, and insulin sensitivity; it prevents the formation and progression of atherosclerotic plaques, lowers blood pressure and plasma fibrinogen levels, and positively impacts overall cardiac function.\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e Furthermore, the instability of culprit lesions was associated with lipoprotein profile alterations in postmenopausal females, indicating the possibility of tailored lipid-modifying strategies, such as PCSK-9 inhibitors, for managing acute coronary syndrome in this patient population.\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e Middle age is an optimal period for assessing women\\u0026rsquo;s future CAD risk factors and implementing preventive strategies. Myocardial infarctions frequently occur in arteries with mild to moderate stenosis, as initially observed in angiography.\\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e After PCI in individuals with ACS, major adverse cardiovascular events (MACE) throughout follow-up are commonly attributed to recurrent issues in culprit and non-culprit lesions.\\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e This study holds valuable implications for preventing cardiovascular event recurrence, exploring the relationship between female reproductive aging and atherosclerosis, and promoting cardiovascular health in women.\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eStudy Limitations\\u003c/strong\\u003e \\u003cp\\u003eNevertheless, this investigation faces certain limitations. First, menopausal status was broadly categorized due to limited data on physiological and psychological menopausal symptoms, using Brambilla et al.\\u0026rsquo;s definition of perimenopause as a reference standard.\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e This categorization may have resulted in some overlap, as women aged 45\\u0026ndash;55 may include both premenopausal and postmenopausal individuals, introducing misclassification bias. Second, due to the lack of routine sex hormone testing in cardiology, serum estrogen or related biomarkers were not measured across cohorts. Therefore, a detailed analysis of the relationship between menopausal status and coronary atherosclerotic plaque characteristics was not conducted, warranting further research in this field. Second, due to the lack of routine sex hormone testing in cardiology, serum estrogen or related biomarkers were not measured across cohorts. Therefore, a detailed analysis of the relationship between menopausal status and coronary atherosclerotic plaque characteristics was not conducted, warranting further research in this field. Third, the relatively small sample size precluded comprehensive adjustment for potential confounding factors that might influence plaque characteristics. Fourth, the absence of stratified analyses based on conventional cardiovascular risk factors represents, potentially affecting the generalizability of our findings. Fifth, the small sample size may have limited the detection of specific significant differences, highlighting the need for more extensive, prospective studies to validate these findings.\\u003c/p\\u003e \\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eCompared to premenopausal and perimenopausal women with ACS, postmenopausal women with ACS exhibited higher vulnerability in their non-culprit lesions. Understanding the relationship between reproductive aging and atherosclerosis is crucial for preventing recurrent cardiovascular events and supporting cardiovascular health in women.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eClinical trial number:\\u0026nbsp;\\u003c/strong\\u003enot applicable.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate:\\u003c/strong\\u003e The original study was approved by the Ethics Committee of the Second Affiliated Hospital of Harbin Medical University (Reference number: YJSKY2023-090).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication:\\u003c/strong\\u003e All patients provided written informed consent to participate.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials:\\u0026nbsp;\\u003c/strong\\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests:\\u003c/strong\\u003e The authors have no competing interests of interest to declare\\u003cstrong\\u003e.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u003c/strong\\u003e The authors received no specific funding for this work.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions:\\u0026nbsp;\\u003c/strong\\u003eRS designed the study and wrote original draft. CZ, LH, and YZ participated in the OCT analysis. YQ conducted the data. PW, CS, and LC conducted the statistical analyses. LM and BY provided resources and supervised the study. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003cstrong\\u003e:\\u0026nbsp;\\u003c/strong\\u003eThe authors sincerely thank all colleagues and patients who participated in this study.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003ePalmisano BT, Zhu L, Eckel RH, Stafford JM. Sex differences in lipid and lipoprotein metabolism. Mol Metabolism. 2018;15:45\\u0026ndash;55.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eQian J, Maehara A, Mintz GS, et al. Impact of gender and age on in vivo virtual histology-intravascular ultrasound imaging plaque characterization (from the global Virtual Histology Intravascular Ultrasound [VH-IVUS] registry). Am J Cardiol. 2009;103(9):1210\\u0026ndash;4.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVirani SS, Alonso A, Benjamin EJ et al. Heart Disease and Stroke Statistics\\u0026mdash;2020 Update: A Report From the American Heart Association. Circulation 2020;141(9).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNofer JR. Estrogens and atherosclerosis: insights from animal models and cell systems. J Mol Endocrinol. 2012;48(2):R13\\u0026ndash;29.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHuang D, Swanson EA, Lin CP, et al. Optical Coherence Tomography. Science. 1991;254(5035):1178\\u0026ndash;81.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJang I-K, Bouma BE, Kang D-H, et al. Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound. J Am Coll Cardiol. 2002;39(4):604\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePundziute G, Schuijf JD, van Velzen JE, et al. Assessment with multi-slice computed tomography and gray-scale and virtual histology intravascular ultrasound of gender-specific differences in extent and composition of coronary atherosclerotic plaques in relation to age. Am J Cardiol. 2010;105(4):480\\u0026ndash;6.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAD HJFA. A, In vivo diagnosis of plaque erosion and calcified nodule in patients with acute coronary syndrome by intravascular optical coherence tomography. 2013;62(19):1748\\u0026ndash;58.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMunir JA, Wu H, Bauer K, et al. The perimenopausal atherosclerosis transition. Menopause. 2012;19(1):10\\u0026ndash;5.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMangione CM, Barry MJ, Nicholson WK et al. Hormone Therapy for the Primary Prevention of Chronic Conditions in Postmenopausal Persons. JAMA 2022;328(17).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNelson HD, Menopause. Lancet. 2008;371(9614):760\\u0026ndash;70.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTang H, Li Z, Fan Y, et al. Differences in Culprit Lesions Between Premenopausal and Postmenopausal Women With Acute Coronary Syndrome: An Optical Coherence Tomography Study. Can J Cardiol. 2022;38(1):85\\u0026ndash;91.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAraki M, Park SJ, Dauerman HL, et al. Optical coherence tomography in coronary atherosclerosis assessment and intervention. Nat Rev Cardiol. 2022;19(10):684\\u0026ndash;703.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRuiz-Garcia J, Lerman A, Weisz G, et al. Age- and gender-related changes in plaque composition in patients with acute coronary syndrome: the PROSPECT study. EuroIntervention. 2012;8(8):929\\u0026ndash;38.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiu SL, Bajpai A, Hawthorne EA et al. Cardiovascular protection in females linked to estrogen-dependent inhibition of arterial stiffening and macrophage MMP12. JCI Insight 2019;4(1).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAbela GS. Cholesterol crystals piercing the arterial plaque and intima trigger local and systemic inflammation. J Clin Lipidol. 2010;4(3):156\\u0026ndash;64.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGeng Y-J, Phillips JE, Mason RP, Casscells SW. 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J Affect Disord. 2014;156:200\\u0026ndash;5.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Acute coronary syndrome, Women, Optical coherence tomography, Non-culprit plaque\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5965842/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5965842/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eThe features of non-culprit lesions among women in premenopausal, perimenopausal, and postmenopausal stages with acute coronary syndrome (ACS) remain unclear. Optical coherence tomography (OCT) represents a catheter-based imaging technique. This study employed OCT to investigate potential differences in non-culprit lesions among women with ACS across menopausal stages.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eOf 194 patients with ACS who underwent OCT before the intervention, 243 non-culprit plaques were identified. Based on age, patients were categorized as premenopausal (n\\u0026thinsp;=\\u0026thinsp;23), perimenopausal (n\\u0026thinsp;=\\u0026thinsp;37), and postmenopausal (n\\u0026thinsp;=\\u0026thinsp;134) cohorts, non-culprit lesion characteristics were compared across these cohorts.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003ePlaque erosion exhibited higher occurrence in premenopausal women relative to those in perimenopausal and postmenopausal women (44.8% vs. 15.7% vs. 27.0%; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.048). Moreover, fibrous plaques were more frequent in premenopausal women (31.0% vs. 29.4% vs. 13.5%; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.040). The postmenopausal cohort showed a markedly larger mean lipid arc and lipid index in contrast to the premenopausal cohort (174.2\\u0026deg; vs. 126.8\\u0026deg;; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.032 and 1542.7 vs. 627.8; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.025, respectively). Non-culprit lesions in postmenopausal women displayed more vulnerable features, including macrophage presence (62.1% vs. 70.6% vs. 87.1%; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.025), cholesterol crystals (37.9% vs. 51.0% vs. 76.7%; P\\u0026thinsp;=\\u0026thinsp;0.001), and spotty calcification (6.9% vs. 11.8% vs. 52.1%; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.001).\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003ePostmenopausal women with ACS exhibited higher vulnerability in non-culprit lesions compared to their premenopausal and perimenopausal counterparts.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The Differences in Non-Culprit Lesions Among Premenopausal, Perimenopausal, and Postmenopausal Women with Acute Coronary Syndrome\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-03-27 04:37:26\",\"doi\":\"10.21203/rs.3.rs-5965842/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"87396b6e-c131-4bb3-b15c-5c324345f676\",\"owner\":[],\"postedDate\":\"March 27th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-03-31T15:23:41+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-03-27 04:37:26\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5965842\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5965842\",\"identity\":\"rs-5965842\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}