Abstract
Background
Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly recognized as a risk factor for cardiovascular events. However, MASLD’s relationship with optical coherence tomography (OCT)-defined coronary plaque vulnerability in patients with acute coronary syndrome (ACS) remains unclear. Moreover, the mediating role of plaque vulnerability in the association between MASLD and major adverse cardiovascular and cerebrovascular events (MACCE) has rarely been investigated. Therefore, this study used OCT to examine the association between MASLD and coronary plaque vulnerability in ACS patients, to assess the impact of MASLD on MACCE, and to explore whether plaque vulnerability mediates this relationship.
Methods
A total of 820 patients with ACS were enrolled in this study. All patients underwent OCT-guided percutaneous coronary intervention (PCI) and completed abdominal ultrasound (US) or non-contrast computed tomography (CT) examinations. Patients meeting the diagnostic criteria for MASLD were categorized into the MASLD group, while those who did not were assigned to the non-MASLD group.
Results
The MASLD group had a significantly higher prevalence of multivessel disease, more complex coronary artery disease on OCT, and higher detection rates of lipid-rich plaques, cholesterol crystals, and thin-cap fibroatheroma (TCFA). Multivariate analysis confirmed that MASLD was independently associated with these vulnerable plaque features and served as an independent risk factor for major adverse cardiovascular and cerebrovascular events (MACCE) (adjusted hazard ratio [aHR] = 2.05, 95% confidence interval [CI]: 1.34–3.14, P < 0.001). Notably, plaque vulnerability, as represented by cholesterol crystals and TCFA, partially mediated the association between MASLD and MACCE, with mediation proportions of 9.90% and 23.70%, respectively.
Conclusion
In patients with ACS, MASLD is associated with more complex coronary artery disease and increased plaque vulnerability assessed by OCT. Furthermore, MASLD is an independent risk factor for MACCE, and this association is partially mediated by plaque vulnerability.
Keywords: Acute coronary syndrome, Metabolic dysfunction-associated steatotic liver disease, Optical coherence tomography, Plaque vulnerability, Major adverse cardiovascular and cerebrovascular events
Introduction
In recent years, with changes in lifestyle, dietary structure, and improvements in living standards, metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most common chronic liver disease worldwide [1, 2]. Owing to its high incidence and substantial disease burden, MASLD has attracted widespread academic attention for its adverse effects on multiple organs, particularly the cardiovascular system. It is now recognized as a systemic disease [3, 4]. The pathophysiological mechanisms of MASLD mainly include systemic inflammation, lipid metabolism disorder, insulin resistance, enhanced oxidative stress, and endothelial dysfunction, which are highly consistent with the pathogenic mechanisms of traditional cardiovascular risk factors [5, 6]. Multiple studies have confirmed that MASLD is closely associated with an increased risk of cardiovascular disease and has now been recognized as a novel risk factor for atherosclerotic cardiovascular disease [7, 8].
Optical coherence tomography (OCT) is a rapidly evolving intracoronary imaging technique that offers a high spatial resolution of approximately 10 μm compared with coronary computed tomography angiography (CCTA). This enables precise identification of the microstructure and histological features of coronary plaques, conferring unique advantages in assessing plaque vulnerability [9, 10]. OCT-defined plaque vulnerability mainly includes lipid-rich plaques, macrophage infiltration, microvessels, cholesterol crystals, and thin-cap fibroatheroma (TCFA). Existing studies have demonstrated that such plaque characteristics significantly increase the risk of long-term adverse cardiovascular events [11–13]. Therefore, early and accurate identification of plaque vulnerability is of great clinical significance for reducing the incidence of cardiovascular events.
Despite the prominent advantages of OCT in plaque assessment, current studies investigating the association between MASLD and coronary plaque characteristics have mostly been based on CCTA, and high-resolution OCT evidence remains scarce. To address this research gap, the present study aimed to use OCT to investigate the association between MASLD and coronary plaque vulnerability in patients with acute coronary syndrome (ACS), evaluate the impact of MASLD on major adverse cardiovascular and cerebrovascular events (MACCE), and further explore the mediating role of plaque vulnerability in the relationship between MASLD and MACCE.
Materials and methods
Study design and patient characteristics
This was a retrospective, single-center observational study that enrolled consecutive patients with ACS who underwent percutaneous coronary intervention (PCI) and completed abdominal ultrasound (US) or non-contrast computed tomography (CT) at the Affiliated Hospital of Zunyi Medical University from January 2021 to May 2024. All patients underwent preoperative OCT examination of the culprit lesions, which were identified by experienced cardiologists based on a comprehensive assessment of electrocardiographic changes, imaging characteristics, physiological evaluation, and the severity of angiographic stenosis. A total of 1064 patients were initially enrolled. The final study cohort was established after excluding participants who met any of the following criteria, and 820 patients were finally included in the analysis: (1) prior PCI (n = 101); (2) excessive alcohol consumption (n = 47); (3) viral hepatitis B/C, autoimmune liver disease, drug-induced liver injury, hereditary liver disease, or malignant tumor (n = 39); (4) incomplete clinical data or poor-quality OCT images (n = 40); (5) loss to follow-up (n = 17). All patients received standard dual antiplatelet therapy in accordance with clinical guidelines. A loading dose was administered before the procedure: 300 mg of clopidogrel and 300 mg of aspirin. All patients continued dual antiplatelet therapy for at least 6 months after PCI. The study protocol strictly adhered to the ethical principles of the Declaration of Helsinki (revised in 2013) and was approved by the Ethics Committee of the Affiliated Hospital of Zunyi Medical University (Approval No.: KLLY-2024-187). Written informed consent was obtained from all participants prior to enrollment. The flow diagram of patient inclusion and exclusion is shown in Fig. 1.
Fig. 1.
Study flow chart. Abbreviations: ACS, acute coronary syndrome; OCT, optical coherence tomography; PCI, percutaneous coronary intervention; US: ultrasound; CT, computed tomography; MASLD, metabolic dysfunction-associated steatotic liver disease
Definition of MASLD
Steatotic liver disease (SLD) was diagnosed based on abdominal US or non-contrast abdominal CT findings. For abdominal US, the diagnosis was based on any of the following findings: (1) enhanced echo signals in the near-field of the liver; (2) unclear visualization of intrahepatic vessel structures; (3) attenuation of echo signals in the far-field of the liver. For non-contrast abdominal CT, the region of interest (ROI) method was used to measure the CT attenuation values of the liver and spleen. Two ROIs were placed in the right hepatic lobe and one in the spleen, and the liver/spleen CT value ratio (L/S ratio) was calculated. An L/S ratio < 1.0 was diagnostic for SLD [14]. All US examinations were performed using a Mindray Resona R9G ultrasound scanner (Mindray, Shenzhen, China) equipped with an SC6-1U convex probe. All CT examinations were performed using a Siemens syngo CT 2012B scanner (Siemens Healthineers, Erlangen, Germany) with a slice thickness of 5.0 mm. All participants underwent scans following a standardized protocol, and the images were subsequently re-evaluated by a second board-certified radiologist.
MASLD was defined as the presence of SLD diagnosed by abdominal US or non-contrast CT, after excluding excessive alcohol consumption (≥ 140 g/week for females and ≥ 210 g/week for males) and other liver diseases, in combination with at least one of the following metabolic abnormalities: (1) body mass index (BMI) ≥ 23 kg/m²; (2) fasting plasma glucose ≥ 5.6 mmol/L or diagnosed type 2 diabetes; (3) blood pressure ≥ 130/85 mmHg or specific antihypertensive drug treatment; (4) plasma levels of triglycerides ≥ 1.70 mmol/L or lipid-lowering treatment; (5) plasma levels of high-density lipoprotein cholesterol (HDL-C) ≤ 1.0 mmol/L (males) or ≤ 1.3 mmol/L (females) or lipid-lowering treatment [15]. Representative abdominal US and non-contrast CT images of SLD are provided in Fig. 2.
Fig. 2.
Typical imaging findings of SLD. A Abdominal US image demonstrating enhanced echogenicity of the liver consistent with SLD (L: liver; K: kidney). B Non-contrast CT image showing the measurement of hepatic and splenic attenuation values in Hounsfield units (HU) using ROIs. Two ROIs were placed in the right hepatic lobe (27.34 HU and 24.14 HU) and one ROI in the spleen (44.66 HU). The L/S ratio was < 1.0, consistent with SLD. Abbreviations: SLD, steatotic liver disease; US, ultrasound; CT, computed tomography; HU, Hounsfield units; ROI, region of interest; L/S, liver-to-spleen ratio
Data collection and definitions
Baseline clinical data and laboratory test results were systematically extracted from electronic medical records by two independent investigators who were blinded to the study purpose. The clinical data included patients’ demographic characteristics (age, sex, systolic blood pressure, and BMI) and past medical history (smoking, diabetes mellitus, and hypertension). Laboratory test indicators comprised white blood cell count (WBC), hemoglobin level, platelet count, total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), HDL-C, alanine aminotransferase, aspartate aminotransferase, fasting blood glucose, estimated glomerular filtration rate (eGFR), N-terminal pro-B-type natriuretic peptide, creatine kinase-MB (CK-MB), and high-sensitivity cardiac troponin T (The normal reference value is less than 14 ng/L). The global registry of acute coronary events (GRACE) risk score [16] and left ventricular ejection fraction (LVEF) were also collected.
BMI was calculated as body weight (kg) divided by the square of height (m²). Current smokers were defined as individuals who had not quit smoking or who had quit smoking less than 15 years ago with a cumulative smoking history of > 10 pack-years [17]. Hypertension was defined as either of the following: (1) resting blood pressure ≥ 140/90 mmHg on three separate measurements, or (2) a prior diagnosis of hypertension with ongoing antihypertensive treatment [18]. The diagnosis of diabetes was based on one of the following: (1) self-reported use of anti-diabetic medications, or (2) meeting any of the following glycemic criteria: random plasma glucose ≥ 11.1 mmol/L, fasting plasma glucose ≥ 7.0 mmol/L, or 2-hour plasma glucose ≥ 11.1 mmol/L following a 75-g oral glucose tolerance test [19]. The diagnosis of ACS was established according to current clinical practice guidelines. ACS comprises three subtypes: unstable angina, non-ST-segment elevation myocardial infarction, and ST-segment elevation myocardial infarction [20].
Intracoronary imaging acquisition
All patients underwent preoperative imaging evaluation of culprit coronary artery plaques using frequency-domain OCT. OCT imaging catheters were advanced to 5–10 mm distal to the culprit lesion for image acquisition, and contrast medium was used to clear blood from the coronary artery during OCT pullback. The acquired images were stored and analyzed offline using the LightLab OCT system (LightLab Imaging, Westford, MA, USA). Subsequently, two experienced analysts blinded to patients’ clinical data performed automated characterization of culprit plaques using dedicated quantitative analysis software (OctPlus, version 2.0, Pulse Medical Imaging Technology). In cases of interobserver discrepancies between the two primary analysts, a third independent analyst reviewed the images to reach a consensus. Interobserver agreement for categorical variables of culprit lesion classification was assessed using Cohen’s kappa test (κ = 0.75, 95% CI: 0.69–0.83), while the intraclass correlation coefficient was 0.79 (95% CI: 0.71–0.87) for continuous variables. The analysis was divided into pre-procedural and post-procedural OCT characteristics. For pre-procedural OCT quantitative assessment, the evaluated parameters included lesion length, pre-procedural minimal lumen area (MLA), plaque volume, plaque burden, minimal fibrous cap thickness, and lipid maximum angle. Pre-procedural OCT qualitative assessment covered the detection of fibrous plaques, lipid-rich plaques, calcific plaques, macrophage infiltration, microvessels, cholesterol crystals, thin-cap fibroatheroma (TCFA), plaque rupture and plaque erosion. Fibrous plaques were defined as homogeneous, high-signal regions [11]. Lipid tissue was described as low-signal areas with ill-defined or blurred borders, and lipid-rich plaques were diagnosed when the lipid maximum angle was greater than 180° [21]. Calcific plaques presented as strongly reflective high-signal areas with posterior signal attenuation and acoustic shadowing, with relatively clear boundaries [22]. Macrophage infiltration was characterized by punctate or strip-like high-reflection foci accompanied by posterior shadowing, which were commonly located at the interface between the fibrous cap and inner lipid core [23]. Microvessels were defined as low-signal vesicular or tubular structures visible on no less than three consecutive OCT frames [24]. TCFA was defined as plaques with a lipid maximum angle > 180° and a minimal fibrous cap thickness < 65 μm. Plaque rupture was identified as fibrous cap discontinuity combined with intraplaque cavity formation [25]. Plaque erosion was diagnosed based on the following manifestations: attached thrombus overlying an intact plaque, irregular luminal surface of culprit lesions without visible thrombus, or thrombus-induced signal attenuation of the underlying plaque, in the absence of superficial lipid or calcification adjacent to the thrombus [26]. Post-procedural OCT and procedural parameters consisted of post- MLA, mean stent size, total stent length and stent expansion ratio. Revascularization strategies were classified into drug-eluting stents (DES), drug-coated balloon only (DCB-only) and plain old balloon angioplasty (POBA). Representative OCT images are shown in Fig. 3.
Fig. 3.
Representative OCT images. A Cholesterol crystals. B Macrophage infiltration. C Microvessels. D TCFA. E Plaque rupture. F Plaque erosion. Abbreviations: OCT, optical coherence tomography; TCFA, thin‑cap fibroatheroma
Patient follow-up
Follow-up was conducted by trained physicians primarily via telephone interviews and review of hospital medical records. The primary outcome was MACCE, defined as a composite of stroke, cardiovascular mortality, non-fatal myocardial infarction, unplanned revascularization, readmission due to heart failure (HF) and readmission due to angina pectoris (AP). Based on the findings of previous studies [27, 28], stroke was defined as an acute focal neurological deficit syndrome induced by ischemic cerebrovascular occlusion or hemorrhagic cerebrovascular rupture, which results in the interruption or abnormal perfusion of local cerebral blood flow. Cardiovascular death referred to death caused by malignant arrhythmia, acute myocardial infarction, HF, or other cardiac disorders, as well as unexplained death where cardiac etiologies could not be ruled out. Non-fatal myocardial infarction required typical myocardial ischemic symptoms accompanied by elevated cardiac biomarkers or dynamic electrocardiographic changes. Unplanned revascularization is defined [29] as a repeat percutaneous coronary intervention or coronary artery bypass grafting performed after hospital discharge. Such reinterventions occur when patients present with ischemic symptoms or angiographically verified lesion progression, irrespective of whether angina pectoris is present. All patients with acute coronary syndrome and multivessel coronary artery disease enrolled in this study underwent culprit vessel treatment during the index procedure to alleviate myocardial ischemia promptly. For non-culprit lesions with proven ischemia and anatomically suitable for intervention, in-hospital staged percutaneous coronary intervention was implemented routinely. Staged intervention targeting non-culprit lesions during the initial hospital stay was classified as planned revascularization and excluded from the statistical analyses of unplanned revascularization. Readmission due to HF refers to hospital readmission triggered by clinical manifestations of heart failure. Readmission due to AP is described as hospital readmission resulting from new-onset or aggravated chest pain and chest tightness. Upon admission assessment, no revascularization was indicated, and patients were managed solely with intensified medical therapy and symptomatic observation.
Statistical analyses
Continuous variables were assessed for normality using the Kolmogorov–Smirnov test. As none of the variables followed a normal distribution, they are presented as median (Q1, Q3) and compared using the Mann–Whitney U test. Between-group comparisons for categorical variables were performed using the chi-square test or Fisher’s exact test. Univariate and multivariate logistic regression analyses were performed to identify independent factors associated with OCT plaque characteristics, with results reported as odds ratios (ORs) and 95% confidence intervals (CIs). Variables with P < 0.10 in univariate analysis were candidates for the multivariate model. Before fitting the multivariate model, multicollinearity was assessed using the variance inflation factor (VIF); all included variables had VIF < 10, indicating no significant multicollinearity. The Kaplan–Meier method was used to calculate the cumulative incidence of MACCE in the non‑MASLD group and the MASLD group, and Kaplan–Meier cumulative risk curves were plotted. The log–rank test was applied to compare whether the differences in curves between the two groups were statistically significant. Multivariate Cox proportional hazards regression was performed to evaluate the association between MASLD and MACCE, with results reported as hazard ratios (HRs) and 95% CIs. Model 1 was unadjusted. Model 2 was adjusted for traditional risk factors for coronary heart disease, including age, sex, current smoking, hypertension, diabetes mellitus, and BMI. Model 3 was further adjusted for WBC, LDL-C, uric acid, and eGFR, in addition to all variables included in Model 2. Based on previous studies [30, 31], all the included variables are high-risk factors for MACCE in patients with ACS. Additionally, subgroup analyses stratified by baseline characteristics were conducted to assess the robustness of the association between MASLD and MACCE. The analyses were performed across the following prespecified subgroups: sex (male vs. female), age (≤ 65 vs. > 65 years), BMI (< 25 vs. ≥ 25 kg/m²), current smoking (yes vs. no), diabetes mellitus (yes vs. no), hypertension (yes vs. no) and STEMI (yes vs. no). Interaction terms between MASLD status and subgroup variables were tested using likelihood ratio tests, and the results were presented as hazard ratios with 95% CIs in a forest plot. Mediation analysis with 5,000 bootstrap resamples was performed to investigate the mediating role of plaque vulnerability in the association between MASLD and MACCE. The 95% confidence interval for the indirect effect was estimated, and the mediation effect was considered statistically significant if the interval did not contain zero. Subgroup and mediation analyses were adjusted for the same set of confounders as Model 3 in the Cox regression: age, sex, current smoking, hypertension, diabetes mellitus, BMI, WBC, LDL‑C, uric acid, and eGFR. All tests were two-tailed, and statistical significance was defined as P < 0.05. All statistical analyses were performed using GraphPad Prism (version 9.5.1) and R version 4.4.1.
Results
Baseline clinical and laboratory characteristics
This study enrolled a total of 820 patients with ACS. These patients were stratified into two groups based on the diagnostic criteria for MASLD: the MASLD group (n = 295) and the non-MASLD group (n = 525). The baseline characteristics are summarized in Table 1. Compared with patients in the non-MASLD group, those in the MASLD group had significantly higher systolic blood pressure, BMI, and prevalence of hypertension and diabetes mellitus, as well as elevated serum levels of triglycerides, total cholesterol, LDL-C, fasting plasma glucose, uric acid, alanine aminotransferase, and aspartate aminotransferase. In contrast, the MASLD group was younger and had lower serum HDL-C levels. Additionally, the utilization rate of sodium-glucose linked transporter 2 inhibitors at discharge was significantly higher in the MASLD group (all P < 0.05). There were no significant differences between the two groups in terms of sex distribution, current smoking, clinical diagnosis of ACS, CK-MB, GRACE score, LVEF and other biochemical indices (all P > 0.05).
Table 1.
Baseline clinical and laboratory characteristics
| Total (n = 820) | Non-MASLD (n = 525) | MASLD (n = 295) | P value | |
|---|---|---|---|---|
| Age, years | 60.00 (53.00, 69.00) | 60.00 (54.00, 70.00) | 57.00 (50.00, 66.00) | < 0.001 |
| Male, n(%) | 607 (74.02) | 395 (75.24) | 212 (71.86) | 0.290 |
| SBP, mmHg | 125.00 (114.00, 139.00) | 125.00 (112.00, 137.00) | 127.00 (116.00, 143.00) | 0.010 |
| BMI, kg/m² | 24.22 (22.04, 26.30) | 22.86 (21.40, 24.84) | 25.91 (24.28, 27.49) | < 0.001 |
| Current smoker, n (%) | 486 (59.27) | 303 (57.71) | 183 (62.03) | 0.227 |
| Hypertension, n (%) | 479 (58.41) | 286 (54.48) | 193 (65.42) | 0.002 |
| Diabetes mellitus, n (%) | 184 (22.44) | 75 (14.29) | 109 (36.95) | < 0.001 |
| Clinical diagnosis | 0.434 | |||
| UA | 303 (36.95) | 189 (36.00) | 114 (38.64) | 0.452 |
| NSTEMI, n (%) | 253 (30.85) | 157 (29.90) | 96 (32.54) | 0.433 |
| STEMI, n (%) | 264 (32.20) | 164 (31.24) | 100 (33.90) | 0.434 |
| Laboratory findings | ||||
| WBC, 10^9/L | 7.56 (5.97, 9.55) | 7.67 (5.93, 9.89) | 7.21 (6.11, 9.32) | 0.219 |
| Hb, g/L | 142.00 (127.00, 152.00) | 141.00 (127.00, 152.00) | 143.00 (128.00, 153.00) | 0.290 |
| PLT, 10^9/L | 204.00 (161.00, 242.00) | 201.00 (162.00, 244.00) | 205.00 (160.00, 240.00) | 0.910 |
| TC, mmol/L | 4.95 (4.20, 5.75) | 4.90 (4.20, 5.58) | 5.04 (4.20, 6.04) | 0.024 |
| TG, mmol/L | 1.79 (1.25, 2.62) | 1.69 (1.19, 2.40) | 2.01 (1.44, 3.08) | < 0.001 |
| LDL-C, mmol/L | 2.93 (2.36, 3.49) | 2.90 (2.33, 3.41) | 3.03 (2.42, 3.67) | 0.026 |
| HDL-C, mmol/L | 1.11 (0.97, 1.28) | 1.13 (0.99, 1.32) | 1.08 (0.93, 1.23) | < 0.001 |
| ALT, U/L | 27.00 (20.00, 37.00) | 24.00 (18.00, 32.00) | 33.00 (24.00, 42.00) | < 0.001 |
| AST, U/L | 29.00 (23.75, 38.25) | 28.00 (24.00, 37.00) | 30.00 (23.50, 42.00) | 0.035 |
| FPG, mmol/L | 6.29 (5.17, 7.91) | 6.07 (5.08, 7.46) | 6.70 (5.44, 9.29) | < 0.001 |
| Uric acid, µmol/L | 372.00 (301.50, 428.25) | 365.00 (284.00, 423.00) | 377.00 (327.50, 439.00) | 0.017 |
| Creatinine, µmol/L | 79.00 (67.00, 92.25) | 79.00 (67.00, 92.00) | 78.00 (66.00, 93.50) | 0.873 |
| eGFR, ml/min/1.73m2 | 89.10 (69.90, 104.86) | 89.70 (69.64, 104.92) | 88.85 (70.60, 104.54) | 0.853 |
| NT-ProBNP, ng/L | 219.00 (74.00, 610.50) | 234.00 (88.00, 581.00) | 207.00 (58.00, 724.50) | 0.425 |
| hs-cTnT, pg/mL | 26.30 (8.93, 296.50) | 26.30 (8.96, 270.90) | 26.30 (8.91, 341.00) | 0.890 |
| CK-MB, U/L | 17.00 (13.00,30.00) | 17.00 (13.00,28.00) | 18.00 (14.00,34.50) | 0.373 |
| LVEF, % | 58.00 (51.00,61.00) | 59.00 (53.00,61.00) | 57.00 (50.00,61.00) | 0.105 |
| GRACE score | 122.00 (112.00,138.25) | 122.00 (108.00,141.00) | 123.00 (118.50,130.00) | 0.253 |
| Medications at discharge | ||||
| AspirinaIndobufen, n (%) | 808 (98.54) | 516 (98.29) | 292 (98.98) | 0.621 |
| P2Y12 inhibitor, n (%) | 812 (99.02) | 520 (99.05) | 292 (98.98) | 1.000 |
| Statin, n (%) | 807 (98.41) | 519 (98.86) | 288 (97.63) | 0.288 |
| ACEI/ARB/ARNI, n (%) | 612 (74.63) | 386 (73.52) | 226 (76.61) | 0.330 |
| β-Blocker, n (%) | 630 (76.83) | 400 (76.19) | 230 (77.97) | 0.563 |
| SGLT2i, n (%) | 142 (17.32) | 73 (13.90) | 69 (23.39) | < 0.001 |
| PCSK9i, n (%) | 182 (22.20) | 106 (20.19) | 76 (25.76) | 0.065 |
Data are presented as median (interquartile range) or n (%)
Abbreviations: MASLD Metabolic dysfunction-associated steatotic liver disease, SBP Systolic blood pressure, BMI Body mass index, STEMI ST-elevation myocardial infarction, NSTEMI Non-ST-elevation myocardial infarction, UA Unstable angina, WBC White blood cell, Hb Hemoglobin; PLT Platelet, TC Total cholesterol, TG Triglycerides, LDL-C Low-density lipoprotein cholesterol, HDL-C High-density lipoprotein cholesterol, ALT Alanine aminotransferase, AST Aspartate aminotransferase, FPG Fasting plasma glucose, eGFR estimated glomerular filtration rate, NT-proBNP N-terminal pro-B-type natriuretic peptide, hs-cTnT high-sensitivity cardiac troponin T, CK-MB Creatine kinase-MB, LVEF Left ventricular ejection fraction, GRACE global registry of acute coronary events, ACEI/ARB/ARNI Angiotensin-converting enzyme inhibitor/angiotensin receptor blocker/angiotensin receptor-neprilysin inhibitor, SGLT2i Sodium-glucose linked transporter 2 inhibitor, PCSK9i Proprotein convertase subtilisin/kexin type 9 inhibitor
Coronary angiographic analysis
As shown in Table 2, coronary angiography revealed that the MASLD group had a significantly higher prevalence of multivessel disease than the non-MASLD group (54.24% vs. 46.67%, P = 0.037). In contrast, no statistically significant differences were observed between the two groups with respect to culprit vessel, lesion segment, and thrombolysis in myocardial infarction flow grade (all P > 0.05).
Table 2.
Coronary angiographic analysis
| Total (n = 820) | Non-MASLD (n = 525) | MASLD (n = 295) | P value | |
|---|---|---|---|---|
| Culprit vessel | ||||
| Left anterior descending artery, n (%) | 483 (58.90) | 312 (59.43) | 171 (57.97) | 0.683 |
| Left circumflex artery, n (%) | 127 (15.49) | 78 (14.86) | 49 (16.61) | 0.505 |
| Right coronary artery, n (%) | 210 (25.61) | 135 (25.71) | 75 (25.42) | 0.927 |
| Lesion segment | ||||
| Proximal, n (%) | 347 (42.32) | 225 (42.86) | 122 (41.36) | 0.676 |
| Middle, n (%) | 376 (45.85) | 242 (46.10) | 134 (45.42) | 0.853 |
| Distal, n (%) | 97 (11.83) | 58 (11.05) | 39 (13.22) | 0.355 |
| TIMI flow grade | 0.521 | |||
| 0/1, n (%) | 168 (20.49) | 104 (19.81) | 64 (21.69) | |
| 2/3, n (%) | 652 (79.51) | 421 (80.19) | 231 (78.31) | |
| Coronary artery lesions | 0.037 | |||
| SVD, n (%) | 415 (50.61) | 280 (53.33) | 135 (45.76) | |
| MVD, n (%) | 405 (49.39) | 245 (46.67) | 160 (54.24) |
Data are presented as n (%)
Abbreviations: MASLD Metabolic dysfunction-associated steatotic liver disease, TIMI Thrombolysis in myocardial infarction, SVD Singlevessel disease, MVD Multivessel disease
OCT analysis
Table 3 presents both quantitative and qualitative analyses of OCT findings. For quantitative assessment, compared with the non-MASLD group, the MASLD group exhibited a longer lesion length [23.00 (18.90, 25.60) mm vs. 20.40 (16.90, 25.80) mm, P < 0.001], a smaller minimal lumen area [1.40 (1.12, 1.63) mm² vs. 1.52 (1.14, 1.79) mm², P = 0.007], a higher plaque burden [56.40 (51.40, 63.90)% vs. 55.40 (50.70, 60.80)%, P = 0.037], a thinner minimal fibrous cap thickness (FCT) [86.00 (61.00, 119.50) µm vs. 104.00 (69.00, 138.00) µm, P < 0.001], and a lipid max angle [211.50 (174.70, 256.45)° vs. 199.00 (158.90, 244.40)°, P < 0.001]. By contrast, no significant differences were detected in plaque volume [109.90 (85.80, 136.15) mm³ vs. 105.50 (87.00, 137.20) mm³, P = 0.284] (Fig. 4). Regarding OCT qualitative assessment, the MASLD group had a significantly higher prevalence of lipid-rich plaques (72.20% vs. 58.29%, P < 0.001), cholesterol crystals (60.00% vs. 46.29%, P < 0.001), and TCFA (32.20% vs. 20.95%, P < 0.001); no significant between-group differences were observed in the rates of fibrous plaques, calcific plaques, macrophage infiltration, microvessels, plaque rupture, or plaque erosion (all P > 0.05) (Fig. 5). The post- MLA and stent expansion ratio were comparable between the two groups (P > 0.05). Nevertheless, the MASLD group received a significantly larger mean stent size [3.25 (3.00, 3.75) mm vs. 3.00 (2.85, 3.50) mm, P < 0.001] and longer total stent length [38.00 (29.00, 53.00) mm vs. 33.00 (25.00, 40.00) mm, P < 0.001]. In terms of revascularization strategies, the proportions of DES, only-DEB and POBA showed no statistical differences between groups (all P > 0.05). (Fig. 6).
Table 3.
Optical coherence tomography analysis
| Total (n = 820) | Non-MASLD (n = 525) | MASLD (n = 295) | P value | |
|---|---|---|---|---|
| Pre-procedural OCT characteristics | ||||
| Quantitative assessment | ||||
| Lesion length, mm | 21.40 (17.60, 25.60) | 20.40 (16.90, 25.80) | 23.00 (18.90, 25.60) | < 0.001 |
| Pre-MLA, mm² | 1.47 (1.13, 1.76) | 1.52 (1.14, 1.79) | 1.40 (1.12, 1.63) | 0.007 |
| Plaque volume, mm³ | 107.70 (86.45, 136.68) | 105.50 (87.00, 137.20) | 109.90 (85.80, 136.15) | 0.284 |
| Plaque burden, % | 56.10 (51.20, 61.73) | 55.40 (50.70, 60.80) | 56.40 (51.40, 63.90) | 0.037 |
| Minimal FCT, µm | 98.00 (64.00, 133.00) | 104.00 (69.00, 138.00) | 86.00 (61.00, 119.50) | < 0.001 |
| Lipid max angle, ° | 201.66 (160.90, 248.83) | 199.00 (158.90, 244.40) | 211.50 (174.70, 256.45) | < 0.001 |
| Qualitative assessment | ||||
| Fibrous plaque, n(%) | 536 (65.37) | 340 (64.76) | 196 (66.44) | 0.628 |
| Lipid-rich plaque, n (%) | 519 (63.29) | 306 (58.29) | 213 (72.20) | < 0.001 |
| Calcific plaque, n (%) | 428 (52.20) | 275 (52.38) | 153 (51.86) | 0.887 |
| Macrophage infiltration, n (%) | 454 (55.37) | 287 (54.67) | 167 (56.61) | 0.591 |
| Microvessels, n (%) | 292 (35.61) | 178 (33.90) | 114 (38.64) | 0.174 |
| Cholesterol crystal, n (%) | 420 (51.22) | 243 (46.29) | 177 (60.00) | < 0.001 |
| TCFA, n (%) | 205 (25.00) | 110 (20.95) | 95 (32.20) | < 0.001 |
| Plaque rupture, n (%) | 329 (40.12) | 204 (38.86) | 125 (42.37) | 0.324 |
| Plaque erosion, n (%) | 240 (29.27) | 161 (30.67) | 79 (26.78) | 0.240 |
| Post-procedural OCT characteristics | ||||
| Post-MLA, mm² | 4.98 (4.01,6.33) | 4.98 (4.00,6.47) | 4.97 (4.08,6.26) | 0.669 |
| Mean stent size, mm | 3.12 (2.88,3.50) | 3.00 (2.85,3.5.00) | 3.25 (3.00,3.75) | < 0.001 |
| Total stent length, mm | 34.00 (26.00,46.00) | 33.00 (25.00,40.00) | 38.00 (29.00,53.00) | < 0.001 |
| Stent expansion ratio, % | 86.00 (81.00,90.00) | 86.00 (81.00,90.00) | 86.00(81.50,91.00) | 0.394 |
| DES, n(%) | 770 (93.90) | 490 (93.33) | 280 (94.42) | 0.364 |
| Only-DEB, n(%) | 45 (5.49) | 27 (5.14) | 18 (6.10) | 0.563 |
| POBA, n(%) | 5 (0.61) | 2 (0.38) | 3 (1.02) | 0.512 |
Data are presented as median (interquartile range) or n (%)
Abbreviations: MASLD Metabolic dysfunction-associated steatotic liver disease, MLA Minimal lumen area, FCT Minimal fibrous cap thickness, TCFA Thin-cap fibroatheroma, DES Drug-Eluting Stent, DEB Drug-coated balloon, POBA Plain old balloon angioplasty
Fig. 4.
Pre-procedural OCT quantitative analysis between the non-MASLD and MASLD groups. Abbreviations: OCT, optical coherence tomography; MASLD, metabolic dysfunction-associated steatotic liver disease; MLA, minimal lumen area; FCT, fibrous cap thickness. The results showed that compared with the Non-MASLD group, patients in the MASLD group had longer lesion length, smaller Pre-MLA, greater plaque burden, thinner minimal FCT and a larger maximum lipid angle, whereas plaque volume exhibited no significant intergroup difference
Fig. 5.
Pre-procedural OCT qualitative analysis between the non-MASLD and MASLD groups. Abbreviations: OCT, optical coherence tomography; MASLD, metabolic dysfunction-associated steatotic liver disease; TCFA, thin-cap fibroatheroma
Fig. 6.
Post-procedural OCT characteristics between the non-MASLD and MASLD groups. Abbreviations: OCT, optical coherence tomography; MASLD, metabolic dysfunction-associated steatotic liver disease; MLA, minimal lumen area. The results demonstrated that compared with the Non-MASLD cohort, the MASLD group achieved a larger mean stent size and greater total stent length. By contrast, no statistically significant intergroup disparities were detected in post-procedural minimal lumen area (Post-MLA) as well as the stent expansion ratio
Univariate and multivariate logistic analysis of OCT plaque characteristics
Univariate and multivariate logistic regression analyses were performed to evaluate the associations of clinical factors with OCT plaque characteristics, focusing on lipid-rich plaques, cholesterol crystals, and TCFA.
For lipid-rich plaques, multivariate logistic regression analysis showed that MASLD (adjusted odds ratio [aOR] = 1.79, 95% CI: 1.24–2.58, P = 0.002) and current smoking (aOR = 1.52, 95% CI: 1.13–2.03, P = 0.005) were independent risk factors. In contrast, diabetes mellitus and BMI were not significantly associated after adjustment (Table 4). For cholesterol crystals, multivariate logistic regression analysis revealed that MASLD (aOR = 1.58, 95% CI: 1.17–2.13, P = 0.003) and diabetes mellitus (aOR = 1.43, 95% CI: 1.01–2.03, P = 0.042) were independent risk factors (Table 5). For TCFA, the results demonstrated that MASLD (aOR = 1.59, 95% CI: 1.09–2.32, P = 0.017) was an independent risk factor, whereas BMI was not significantly associated after adjustment (Table 6).
Table 4.
Univariate and multivariate logistic regression analysis for lipid-rich plaques
| Univariable | Multivariable | |||
|---|---|---|---|---|
| Influence factor | OR (95% CI) | P value | OR (95% CI) | P value VIF |
| MASLD | 1.86 (1.37–2.53) | < 0.001 | 1.79 (1.24–2.58) | 0.002 1.415 |
| Male | 0.90 (0.65–1.24) | 0.529 | ||
| Age, years | 0.99 (0.98-1.00) | 0.125 | ||
| Current smoking | 1.52 (1.14–2.03) | 0.004 | 1.52 (1.13–2.03) | 0.005 1.007 |
| Hypertension | 0.91 (0.68–1.22) | 0.540 | ||
| Diabetes mellitus | 1.48 (1.04–2.01) | 0.030 | 1.30 (0.90–1.89) | 0.167 1.089 |
| BMI | 1.05 (1.01–1.10) | 0.041 | 0.99 (0.94–1.05) | 0.747 1.394 |
| WBC | 1.00 (0.95–1.05) | 0.916 | ||
| LDL-C | 1.06 (0.98–1.27) | 0.488 | ||
| Uric acid | 1.00 (0.99-1.00) | 0.299 | ||
| eGFR | 1.01 (0.99–1.01) | 0.718 | ||
Abbreviations: OR Odds ratio, CI Confidence interval, VIF Variance inflation factor, MASLD Metabolic dysfunction-associated steatotic liver disease, BMI Body mass index, WBC White blood cell, LDL-C Low-density lipoprotein cholesterol, eGFR Estimated glomerular filtration rate
Table 5.
Univariate and multivariate logistic regression analysis for cholesterol crystal
| Univariable | Multivariable | |||
|---|---|---|---|---|
| Influence factor | OR (95% CI) | P value | OR (95% CI) | P value VIF |
| MASLD | 1.74 (1.30–2.32) | < 0.001 | 1.58 (1.17–2.13) | 0.003 1.082 |
| Male | 1.05 (0.77–1.43) | 0.762 | ||
| Age, years | 1.01 (1.00-1.02) | 0.102 | ||
| Current smoking | 0.92 (0.70–1.22) | 0.577 | ||
| Hypertension | 1.32 (1.00-1.74) | 0.053 | 1.24 (0.93–1.64) | 0.137 1.013 |
| Diabetes mellitus | 1.66 (1.19–2.31) | 0.003 | 1.43 (1.01–2.03) | 0.042 1.074 |
| BMI | 1.03 (0.98–1.07) | 0.244 | ||
| WBC | 1.01 (0.97–1.06) | 0.624 | ||
| LDL-C | 1.06 (0.89–1.25) | 0.513 | ||
| Uric acid | 1.00 (0.99-1.00) | 0.652 | ||
| eGFR | 1.00 (0.99-1.00) | 0.201 | ||
Abbreviations: OR Odds ratio, CI Confidence interval, VIF Variance inflation factor, MASLD Metabolic dysfunction-associated steatotic liver disease, BMI Body mass index, WBC White blood cell, LDL-C Low-density lipoprotein cholesterol, eGFR estimated glomerular filtration rate
Table 6.
Univariate and multivariate logistic regression analysis for TCFA
| Univariable | Multivariable | |||
|---|---|---|---|---|
| Influence factor | OR (95% CI) | P value | OR (95% CI) | P value VIF |
| MASLD | 1.79 (1.30–2.47) | < 0.001 | 1.59 (1.09–2.32) | 0.017 1.373 |
| Male | 0.80 (0.56–1.17) | 0.251 | ||
| Age, years | 1.00 (0.98–1.01) | 0.499 | ||
| Current smoking | 1.13 (0.82–1.56) | 0.460 | ||
| Hypertension | 1.25 (0.90–1.73) | 0.177 | ||
| Diabetes mellitus | 1.08 (0.74–1.57) | 0.699 | ||
| BMI | 1.08 (1.02–1.13) | 0.004 | 1.04 (0.95–1.10) | 0.230 1.373 |
| WBC | 1.02 (0.97–1.07) | 0.439 | ||
| LDL-C | 1.13 (0.94–1.37) | 0.200 | ||
| Uric acid | 1.00 (0.99-1.00) | 0.160 | ||
| eGFR | 1.01 (0.99–1.01) | 0.105 | ||
Abbreviations: TCFA Thin-cap fibroatheroma, OR Odds ratio, CI Confidence interval, VIF Variance inflation factor, MASLD Metabolic dysfunction-associated steatotic liver disease, BMI Body mass index, WBC White blood cell, LDL-C low-density lipoprotein cholesterol, eGFR estimated glomerular filtration rate
Clinical outcome and Kaplan-Meier survival analysis
During a median follow-up of 721 days, 123 patients (15.00%) experienced MACCE. The incidence of MACCE in the MASLD group was significantly higher than that in the non-MASLD group (22.71% vs. 10.67%, P < 0.001) (Table 7; Fig. 7). There were no statistically significant differences between the two groups in the incidences of stroke, cardiovascular mortality, or non-fatal myocardial infarction (all P > 0.05). However, the incidences of unplanned revascularization (8.14% vs. 3.24%, P = 0.002), readmission due to HF (4.41% vs. 1.90%, P = 0.07) and readmission due to AP (6.10% vs. 2.86%, P = 0.023) were both higher in the MASLD group than in the non-MASLD group.
Table 7.
Comparison of MACCE between non-MASLD group and MASLD group
| Total (n = 820) | Non-MASLD (n = 525) | MASLD (n = 295) | P value | |
|---|---|---|---|---|
| MACCE, n(%) | 123 (15.00) | 56 (10.67) | 67 (22.71) | < 0.001 |
| Stroke, n(%) | 14 (1.71) | 8 (1.52) | 6 (2.03) | 0.588 |
| Cardiovascular mortality, n(%) | 6 (0.73) | 2 (0.38) | 4 (1.36) | 0.252 |
| Non-fatal myocardial infarction, n(%) | 6 (0.73) | 2 (0.38) | 4 (1.36) | 0.252 |
| Unplanned revascularization, n(%) | 41 (5.00) | 17 (3.24) | 24 (8.14) | 0.002 |
| Readmission due to HF or AP, n(%) | 56 (6.83) | 27 (5.14) | 29 (9.83) | 0.011 |
Abbreviations: MACCE Major adverse cardiovascular and cerebrovascular events, MASLD Metabolic dysfunction-associated steatotic liver disease, HF or AP, heart failure or angina pectoris
Fig. 7.
Comparison of MACCE between non-MASLD group and MASLD group. Abbreviations: MACCE, major adverse cardiovascular and cerebrovascular events; MASLD, metabolic dysfunction-associated steatotic liver disease; HF or AP, heart failure or angina pectoris
Kaplan-Meier analysis further confirmed a significant difference in the cumulative incidence of MACCE between the two groups (Fig. 8). As shown in Fig. 8A, the overall risk of MACCE was significantly higher in the MASLD group than in the non-MASLD group throughout the follow-up period (HR = 2.18, 95% CI: 1.53–3.11, log-rank P < 0.001). Further analysis of MACCE components indicated that the increased risk of the composite endpoint was primarily driven by higher incidences of unplanned revascularization (HR = 2.58, 95% CI: 1.39–4.81, log-rank P = 0.002; Fig. 8E), readmission due to HF (HR = 2.37, 95% CI: 1.04–5.40, log–rank P = 0.034; Fig. 8F) and readmission due to AP (HR = 2.19, 95% CI: 1.10–4.33, log–rank P = 0.022; Fig. 8F) in the MASLD group. In contrast, there were no statistically significant differences between the two groups in the cumulative incidences of stroke, cardiovascular mortality, or non-fatal myocardial infarction (log-rank P values were 0.566, 0.112, and 0.110, respectively; Fig. 8B and D).
Fig. 8.
Kaplan-Meier cumulative risk curves of MACCE between MASLD and non-MASLD. Abbreviations: MASLD, metabolic dysfunction-associated steatotic liver disease; MACCE, major adverse cardiovascular and cerebrovascular events; HR, hazard ratio; CI, confidence interval; HF, heart failure; AP, angina pectoris. The results demonstrated that MASLD predicted higher MACCE (HR=2.18, 95%CI 1.53–3.11, log-rank P<0.001), mainly driven by increased rates of unplanned revascularization, HF and AP readmissions. Stroke, cardiovascular mortality and non-fatal MI were comparable between the two groups
Association between MASLD and MACCE: multivariate cox regression analysis
Table 8 presents the results of multivariate Cox regression analyses under different adjustment models. In the unadjusted model (Model 1), MASLD was associated with an increased risk of MACCE (HR = 2.18, 95% CI: 1.53–3.11, P < 0.001). In Model 2, after adjusting for age, sex, smoking, hypertension, diabetes, and BMI, MASLD remained independently associated with MACCE (adjusted hazard ratio [aHR] = 2.08, 95% CI: 1.35–3.19, P < 0.001). Further, in Model 3, after adjusting for traditional risk factors as well as WBC, LDL-C, uric acid, and eGFR, MASLD remained independently associated with an increased risk of MACCE (aHR = 2.05, 95% CI: 1.34–3.14, P < 0.001).
Table 8.
Association between MASLD and MACCE by multivariate cox regression analysis
| Model1 | Model2 | Model3 | ||||
|---|---|---|---|---|---|---|
| Variables | HR (95%CI) | P value | HR (95%CI) | P value | HR (95%CI) | P value |
| Non-MASLD | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| MASLD | 2.18(1.53–3.11) | < 0.001 | 2.08(1.35–3.19) | < 0.001 | 2.05(1.34–3.14) | < 0.001 |
Model 1: Crude model
Model 2: Adjusted for age, sex, current smoking, hypertension, diabetes mellitus, BMI
Model 3: Adjusted for age, sex, current smoking, hypertension, diabetes mellitus, BMI, WBC, LDL-C, uric acid, eGFR
Abbreviations: MASLD Metabolic dysfunction-associated steatotic liver disease, MACCE Major adverse cardiovascular and cerebrovascular events, HR Hazard ratio, CI Confidence interval, BMI Body mass index, WBC White blood cell, LDL-C Low-density lipoprotein cholesterol, eGFR estimated glomerular filtration rate
Subgroup analyses for MACCE
Subgroup analyses based on the fully adjusted Model 3 were performed to assess the consistency of the association between MASLD and MACCE across prespecified baseline characteristics. As shown in the forest plot (Fig. 9), the direction of the association was consistent across all examined subgroups. Although statistical significance was not reached in certain subgroups (e.g., females, age > 65 years, BMI < 25 kg/m², and non-smokers), no significant interactions were observed between MASLD status and any subgroup variable (all P for interaction > 0.05). These findings indicate that the independent prognostic effect of MASLD on MACCE is robust across different clinical subgroups.
Fig. 9.
Subgroup analyses for MACCE. Adjusted for age, sex, current smoking, hypertension, diabetes mellitus, BMI, WBC, LDL-C, uric acid, and eGFR. Abbreviations: MASLD, metabolic dysfunction-associated steatotic liver disease; MACCE, major adverse cardiovascular and cerebrovascular events; HR, hazard ratio; BMI, body mass index; WBC, white blood cell; LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; STEMI, ST-segment elevation myocardial infarction
Mediation analysis
To investigate whether coronary plaque vulnerability mediates the association between MASLD and MACCE, we performed mediation analysis with cholesterol crystals, lipid-rich plaques, and TCFA as potential mediators. As shown in Fig. 10, after adjusting for the prespecified covariates included in Model 3, cholesterol crystals (indirect effect β = 0.010, mediation proportion = 9.90%, P = 0.006) and TCFA (indirect effect β = 0.023, mediation proportion = 23.70%, P = 0.032) partially mediated the relationship between MASLD and MACCE. The mediating effect of lipid-rich plaques was not statistically significant (indirect effect β = 0.004, mediation proportion = 3.90%, P = 0.340).
Fig. 10.
Mediating role of OCT plaque characteristics in the association between MASLD and MACCE. A Lipid-rich plaques as mediator; (B) Cholesterol crystals as mediator; (C) TCFA as mediator. Adjusted for age, sex, current smoking, hypertension, diabetes mellitus, BMI, WBC, LDL-C, uric acid, and eGFR. Abbreviations: MASLD, metabolic dysfunction-associated steatotic liver disease; MACCE, major adverse cardiovascular and cerebrovascular events; TCFA, thin-cap fibroatheroma; BMI, body mass index; WBC, white blood cell; LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate;
Discussion
The main findings of this study are as follows: (1) Coronary angiography revealed that the incidence of multivessel disease was significantly higher in the MASLD group than in the non-MASLD group. OCT analysis further showed that patients in the MASLD group had more complex coronary lesions. Moreover, the detection rates of lipid-rich plaques, cholesterol crystals, and TCFA were higher in the MASLD group than in the non-MASLD group. (2) After adjusting for traditional cardiovascular risk factors, MASLD remained an independent risk factor for cholesterol crystals, lipid-rich plaques, and TCFA. (3) The incidence of MACCE was significantly higher in the MASLD group than in the non-MASLD group, and MASLD remained an independent risk factor for MACCE after adjusting for traditional cardiovascular risk factors. Subgroup analysis further confirmed the robustness of the association between MASLD and poor prognosis in patients with ACS. (4) Cholesterol crystals and TCFA partially mediated the association between MASLD and subsequent MACCE, with mediation proportions of 9.90% and 23.70%, respectively, while lipid-rich plaques did not exhibit a significant mediating effect.
Zhang et al. [32], in a CCTA‑based study, reported that the proportion of patients with multivessel disease was significantly higher in individuals with Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD) than in those without MAFLD (16.53% vs. 5.78%, P = 0.004). In the present study, conducted in an ACS population, the incidence of multivessel disease was likewise elevated in the MASLD group (54.24% vs. 46.67%, P = 0.037). A possible explanation for this finding is that patients with MASLD often present with dyslipidemia [33]: intrahepatic triglyceride accumulation promotes the oversecretion of very‑low‑density lipoprotein, thereby providing precursors for small dense low‑density lipoprotein (sdLDL). sdLDL particles exhibit enhanced endothelial permeability and, following oxidative modification, are avidly taken up by scavenger receptors, facilitating foam cell formation and accelerating atherosclerosis [34]. Concurrently, reduced high‑density lipoprotein cholesterol levels impair reverse cholesterol transport and hinder lipid clearance, further exacerbating lipid deposition and driving coronary lesions toward a more complex phenotype [35].
Numerous previous studies based on CCTA have confirmed an association between MASLD and coronary plaque characteristics. Nishihara et al. [28] found that after adjustment for traditional cardiovascular risk factors, MASLD was independently associated with adverse coronary features (obstructive stenosis and/or high-risk plaques) (aOR = 1.31, 95% CI: 1.01–1.71, P = 0.042). De Filippo et al. [36] also reported that patients with MASLD had a significantly higher risk of high-risk plaques (OR = 2.13, 95% CI: 1.42–3.19, P < 0.001), and such plaques more frequently exhibited positive remodeling (OR = 2.92, 95% CI: 1.79–4.77, P < 0.001) and spotty calcification (OR = 2.96, 95% CI: 1.22–7.20, P = 0.020). Other studies [32, 37] have also demonstrated a higher prevalence of non-calcified plaques in patients with MASLD. All these aforementioned studies were based on CCTA; however, this examination has limited spatial resolution and cannot accurately distinguish specific subtypes of non-calcified plaques (e.g., lipid-rich plaques or fibrous plaques). In the present study, using high-resolution OCT, we further demonstrated that non-calcified plaques in patients with MASLD were predominantly lipid‑rich plaques, overcoming the inherent limitations of CCTA. Additionally, our study revealed significantly higher detection rates of cholesterol crystals and TCFA in the MASLD group. After adjustment for relevant confounders, MASLD remained an independent risk factor for lipid-rich plaques, cholesterol crystals, and TCFA. These are all well-established markers of plaque vulnerability, suggesting a close association between MASLD and increased coronary plaque instability. Our results indicated that the incidence of plaque rupture tended to be higher in patients with MASLD than in those without, yet the difference failed to reach statistical significance. This may be explained by selection bias and insufficient sample size. Further large-sample studies are required to verify this finding.
A potential mechanism underlying this association is that hepatic free fatty acid accumulation induces mitochondrial metabolic overload and excessive reactive oxygen species production, thereby triggering chronic inflammation and promoting the release of pro-inflammatory factors including TNF‑α, IL‑6, and CRP [38, 39]. On the one hand, these pro-inflammatory mediators directly impair endothelial function and disrupt endothelial barrier integrity, increasing the risk of plaque erosion and subsequent thrombosis [40, 41]. On the other hand, TNF‑α activates matrix metalloproteinases, which degrade collagen in the fibrous cap, leading to cap thinning and reduced plaque structural stability, thereby increasing the risk of plaque rupture and subsequent MACCE [42].
In addition to the higher coronary plaque vulnerability, a large body of previous studies [43–45] has confirmed the association between MASLD and the occurrence of MACCE. Orzan et al. [43] enrolled 2,038 patients with a mean follow-up of 26.9 months, during which MACCE occurred in 361 patients (17.7%). The incidence of MACCE was significantly higher in the MASLD group than in the non-MASLD group (25.90% vs. 14.71%, P < 0.001), and after adjusting for confounding factors, MASLD remained an independent risk factor for MACCE (aHR = 1.843, 95% CI: 1.475–2.303, P < 0.001). Another large-scale study [44], which included 5,666,728 participants with a mean follow-up of 10.6 years, demonstrated that MASLD was significantly associated with an increased risk of stroke (aHR = 1.12, 95% CI: 1.07–1.17) and HF (aHR = 1.18, 95% CI: 1.15–1.21). Our study only enrolled patients who underwent OCT-guided PCI, while excluding contemporaneous patients treated with Intravascular Ultrasound (IVUS)-guided PCI (n = 3109) and Coronary Angiography (CAG)-guided PCI (n = 4413). This may introduce selection bias. Given that OCT provides superior resolution for evaluating plaque characteristics, we specifically focused on patients receiving OCT-guided PCI and did not include the other two cohorts. As reported by Carvalho PEP et al. [46], the risk of major adverse cardiovascular events (MACE) was significantly lower in patients undergoing OCT-guided or IVUS-guided PCI compared with those receiving CAG-guided PCI. The relative risk (RR) was 0.63 (95% CI: 0.55–0.72, P < 0.001) for the OCT group and 0.67 (95% CI: 0.56–0.79, P < 0.001) for the IVUS group. There was no significant difference in MACE incidence between OCT-guided PCI and IVUS-guided PCI (RR = 0.94, 95% CI: 0.78–1.14, P = 0.52). Considering the limited study population, our results should be interpreted with caution. Large-scale studies are warranted in the future to compare clinical outcomes among ACS patients receiving different PCI guidance strategies. In the present study, although the incidence of MACCE was significantly higher in the MASLD group (primarily driven by increased unplanned revascularization, readmission due to HF and readmission due AP), there were no significant differences between the two groups in the incidence of stroke, non-fatal myocardial infarction, or cardiovascular death. We speculate that this discrepancy may be related to the relatively small sample size and shorter follow-up duration in our study.
Although multiple studies have established an association between MASLD and adverse cardiovascular outcomes, the underlying mediating mechanisms remain incompletely understood. Wang et al. [47] enrolled 367 patients with stable chest pain who underwent CCTA and found that the pericoronary fat attenuation index mediated 46.8% of the association between MAFLD and MACCE, implicating inflammation as a potentially important mediator of the poor prognosis associated with MAFLD. Building on this foundation, the present study focused specifically on an ACS population and, taking advantage of the higher-resolution OCT, further explored potential mediators from the perspective of coronary plaque morphology. Our results demonstrated that, in patients with ACS, cholesterol crystals and TCFA partially mediated the association between MASLD and MACCE, whereas the mediating effect of lipid‑rich plaques was not statistically significant. A possible explanation for this observation is that cholesterol crystals and TCFA represent more advanced and inherently unstable plaque phenotypes. Cholesterol crystals can mechanically disrupt fibrous cap integrity through physical stress, and TCFA is intrinsically recognized as a lesion with an exceptionally high propensity for rupture, thereby accounting for their significant mediating roles in the relationship between MASLD and MACCE. In contrast, lipid‑rich plaques reflect an earlier stage of plaque instability and are ubiquitously present among patients with ACS, which may explain the lack of a statistically significant mediating effect. Collectively, these findings suggest that MASLD may increase the risk of adverse clinical events in patients with ACS by promoting the development of coronary plaque vulnerability, and further reveal that plaque vulnerability serves as a critical bridge linking MASLD with MACCE.
Limitations
First, this was a single-center retrospective study with a relatively small sample size, which may have introduced selection bias. Second, hepatic steatosis was diagnosed using abdominal ultrasound or non-contrast CT rather than liver biopsy, the gold standard, mainly because the latter is costly and invasive, making it unacceptable for most patients. Third, hepatic fibrosis and metabolic dysfunction-associated steatohepatitis were not graded in this study; thus, the differential impact of varying liver disease severity on cardiovascular outcomes could not be determined. Fourth, OCT assessment was limited to culprit lesions without evaluating non-culprit lesions. Since plaque vulnerability may differ between culprit and non-culprit lesions, and non-culprit lesion-related myocardial infarction is a major component of MACCE, future studies are warranted to explore the association between MASLD and non-culprit lesion vulnerability and its impact on long-term prognosis. Fifth, as a retrospective analysis, this study has incomplete documentation of detailed coronary lesion subtypes in historical procedural records. The proportions of high-risk lesions including bifurcation lesions and ostial lesions could not be fully analyzed, which may exert certain impacts on the interpretation of results. We have also clarified the plans for future improvement. In subsequent prospective studies, we will adopt standardized collection of morphological information of coronary lesions at enrollment, and systematically record detailed lesion subtypes such as bifurcation lesions and ostial lesions. This will further improve the analysis of lesion characteristics and enhance the reliability of research conclusions. Finally, the median follow-up duration of this study was 721 days, which is relatively short and may limit the assessment of the long-term risk of MACCE. Owing to the relatively small sample size and short follow-up period, the 95% CIs for some clinical endpoint events were relatively wide, which not only reduces the precision of effect size estimation but may also impair the statistical power of the conclusions. Therefore, the findings of this study need to be further validated in large-scale, multicenter prospective studies to enhance the reliability of the conclusions.
Conclusion
This study demonstrates that among patients with ACS, the presence of MASLD is associated with more complex coronary artery disease and coronary plaque vulnerability as identified by OCT, and serves as an independent risk factor for MACCE. Furthermore, this association is partially mediated by plaque vulnerability.
Acknowledgements
I would like to express my gratitude to all those who helped me during the writing of this manuscript.
Authors’ contributions
M.X. designed the research and wrote the manuscript. Y.D. made rigorous revisions to the manuscript. L.Z., L.H.Z., D.Y., X.L., and R.Z. contributed to data collection and the improvement of the manuscript’s quality. B.S. and J.C. exercised strict control over the quality and logicality of the manuscript. All authors reviewed the manuscript.
Funding
This study was supported by a research grant from Beijing Youde Zhongbang Technology Co., Ltd. (Grant No. ZYHZ-2016-01).
Data availability
The dataset analyzed in the present study is available from the corresponding author upon reasonable request. Contact information of the corresponding author: 2871766504@qq.com.
Declarations
Ethics approval and consent to participate
The study protocol was in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Affiliated Hospital of Zunyi Medical University. All patients signed a written informed consent form to participate in the study prior to any procedures.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Muhan Xu and Yi Deng contributed equally to this work and deserve a conjoint first authorship.
Contributor Information
Bei Shi, Email: shibei2147@163.com.
Jianling Chen, Email: 2871766504@qq.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The dataset analyzed in the present study is available from the corresponding author upon reasonable request. Contact information of the corresponding author: 2871766504@qq.com.