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      Liver Fibrosis Scores as Predictors of Long-term Outcomes in Patients with ST-segment Elevation Myocardial Infarction

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            Abstract

            Background: Liver fibrosis scores (LFSs) are novel tools for predicting cardiovascular events in patients with coronary artery disease. This study was aimed at examining the prognostic value of LFSs in patients with ST-segment elevation myocardial infarction (STEMI).

            Methods: Between 2015 and 2019, 866 patients diagnosed with STEMI were consecutively enrolled. The definition of major cardiovascular events (MACEs) was all-cause death, nonfatal myocardial infarction, nonfatal ischemic stroke, and acute limb ischemia. We evaluated the predictive values of LFSs for MACEs with receiver operating characteristic (ROC) curve and restricted cubic spline (RCS) analysis. Kaplan-Meier (K-M) analysis was conducted to explore the relationship between LFSs and MACEs.

            Results: During a median follow-up of 4 years, 155 MACEs were observed. K-M analysis of MACEs revealed significantly lower event-free survival rates in patients with intermediate or high, rather than low, NFS, FIB-4, BARD, and Forns scores. The multivariable-adjusted hazard ratios (95% CI) for MACEs in patients with high versus low risk scores were 1.343 (0.822–2.197) for NFS, 1.922 (1.085–3.405) for FIB-4, 2.395 (1.115–5.142) for BARD, and 2.271 (1.250–4.125) for Forns. The ROC curve indicated that the predictive ability for MACEs was non significantly improved by addition of the NFS (AUC = 0.7274), FIB-4 (AUC = 0.7199), BARD (AUC = 0.7235), and Forns (AUC = 0.7376) scores into the basic model (AUC = 0.7181). RCS revealed a tendency toward a nonlinear positive association of MACEs with NFS, FIB-4, and particularly Forns scores.

            Conclusion: LFSs have potential utility for predicting adverse outcomes in patients with STEMI, thus indicating the importance of managing metabolic dysregulation.

            Main article text

            List of Abbreviations: AUC, area under the curve; ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin receptor blockers; ALI, acute limb ischemia; BNP, B-type natriuretic peptide; BARD, body mass index, aspartate aminotransferase/alanine aminotransferase ratio, diabetes mellitus score; CAD, coronary artery disease; FIB-4, fibrosis-4; GLP-1 RAs, glucagon-like peptide 1 receptor agonists; HDL-c, high-density lipoprotein cholesterol; K-M, Kaplan-Meier; LFSs, liver fibrosis scores; LDL-c, low-density lipoprotein cholesterol; LVEF, left ventricular ejection fraction; MACEs, major cardiovascular events; MI, myocardial infarction; NT-proBNP, N-terminal pro-B type natriuretic peptide; NAFLD, non-alcoholic fatty liver disease; NFS, non-alcohol fatty liver disease fibrosis score; PPI, proton pump inhibitor; ROC, receiver operating characteristics; RCS, restricted cubic spline; STEMI, ST-segment elevation myocardial infarction; TC, total cholesterol; TG, Triglyceride.

            Background

            Non-alcoholic fatty liver disease (NAFLD) is currently the most common chronic liver disease worldwide. This disease is underdiagnosed because liver biopsy is often unavailable. The presence of fibrosis can be evaluated through liver fibrosis scores (LFSs), which are composed of laboratory parameters, diabetes, age, and BMI. Several common predisposing factors are shared by cardiovascular disease and NAFLD, including obesity, insulin resistance, and hyperlipidemia. Patients with NAFLD have elevated risk of cardiovascular disease [13]. Intriguingly, studies have established a correlation between liver fibrosis, assessed via LFSs, and adverse cardiovascular events across populations, encompassing individuals with and without NAFLD [16]. Moreover, LFSs have been demonstrated to have efficacy as reliable indicators for predicting adverse cardiovascular outcomes in cohorts with previous myocardial infarction or stable coronary heart disease [79]. However, few studies have applied LFSs to the STEMI population.

            To assess the predictive ability and clinical significance of LFSs regarding cardiovascular outcome events in patients with STEMI, we conducted this retrospective study.

            Methods

            Study Design and Population

            This retrospective observational study enrolled patients hospitalized for STEMI at the China-Japan Friendship Hospital between 2015 and 2019. Adult patients diagnosed with STEMI who received revascularization were screened. The exclusion criteria were as follows: (1) patients with established liver diseases, including hepatitis and cirrhosis with definite etiology (e.g., viral hepatitis cirrhosis, schistosomiasis cirrhosis, alcoholic cirrhosis, cholestasis cirrhosis, autoimmune cirrhosis, and toxic/drug cirrhosis); (2) patients with active infections and malignant disease; (3) patients with severe liver and/or renal insufficiency; and (4) patients with hematologic disorders causing thrombocytopenia. A total of 866 patients were enrolled in the study (Figure 1). This cohort study followed the principles of the Declaration of Helsinki, and the study protocol was approved by the Ethics Committee of the China-Japan Friendship Hospital.

            Figure 1

            Flowchart Illustrating the Study Population.

            ACS, acute coronary syndrome; NSTEMI, non-ST-segment elevation myocardial infarction; STEMI, ST-segment elevation myocardial infarction.

            Data Collection

            Demographic parameters and comorbidities, as well as laboratory parameters and medications, were collected for each participant.

            Liver Fibrosis Scores

            The NAFLD-FS was determined with the following formula: NFS = −1.675 + 0.037 × age [years] + 0.094 × BMI [kg/m2] + 1.13 × hyperglycemia/diabetes [yes = 1, no = 0] + 0.99 × (AST [IU/L]/ALT [IU/L]) − 0.013 × platelet count [×109/L] − 0.66 × ALB [g/dL] [10]. Participants were stratified into low (<−1.455), intermediate (−1.455–0.676), and high (>0.676) risk categories according to their scores. The FIB-4 score was determined with the formula FIB-4 = age [years] × AST [IU/L]/(platelets [×109/L] × ALT [IU/L]1/2) [11]. Participants were categorized into low risk (<1.30), intermediate risk (1.30–2.67), and high risk (>2.67) groups. The BARD score was calculated on the basis of BMI ≥ 28 kg/m2 (1 point) + AST/ALT ratio ≥ 0.8 (2 points) + presence of diabetes (1 point), and ranged from 0 to 4 [12]. Participants were classified as low risk (0 or 1 point), intermediate risk (2 points), and high risk (3 or 4 points). The Forns score was calculated as 7.811–3.131 × log (platelet count [109/L]) + 0.781 × log (GGT [IU/L]) + 3.467 log (age [years]) – 0.014 × total cholesterol (mg/dL) [13]. Participants were categorized into three levels as follows: low risk (<4.2), intermediate risk (4.2–6.9), and high risk (>6.9).

            Primary Endpoints and Follow-Up

            The definition of MACEs was a composite of all-cause death, nonfatal myocardial infarction (MI), nonfatal ischemic stroke, and acute limb ischemia (ALI). MI was diagnosed according to the symptoms of myocardial ischemia and the dynamic changes in values of myocardial enzymes (e.g., troponin). Ischemic stroke referred to persistent neurological dysfunction with documentation of acute cerebral infarction on computed tomography and/or magnetic resonance imaging. ALI was ascertained with the following established definition: sudden worsening of limb perfusion requiring hospitalization or treatment with thrombolysis, thrombectomy, or urgent revascularization. All patients were followed up through clinic interviews or telephone calls by well-trained cardiologists or nurses, who were blinded to the study aims. Follow-up continued for all patients until the end of 2021.

            Statistical Analysis

            Continuous variables are presented as mean ± SD or median (interquartile range), as appropriate. Categorical variables are reported as numbers and percentages. Differences between groups were determined with Student’s t test, Mann-Whitney U test, χ2 test, or Fisher’s exact test, as appropriate. Estimation of event-free survival rates was performed with the Kaplan-Meier method, and group comparisons were performed with the log-rank test. Cox proportional hazards models were used for outcome comparisons, and the results are presented as hazard ratios with corresponding 95% confidence intervals. In the multivariate Cox model, adjustments were made for various factors including sex; current smoking status; history of prior stroke; presence of chronic kidney disease; diastolic blood pressure; Killip class; levels of creatinine, D-dimer, peak TnT/TnI, and peak BNP/NT-pro BNP; left ventricular ejection fraction (LVEF); and baseline use of ticagrelor, ACEI/ARB, beta-blockers, diuretics, or PPIs. Notably, adjustments were not performed for the variables already incorporated into the score formula. Receiver operating characteristic (ROC) curve analysis was conducted to determine the area under the curve (AUC) for the LFSs in predicting MACEs. The following parameters were used in the basic model: sex, current smoking, prior history of stroke and chronic kidney disease, diastolic blood pressure, LVEF, heart function based on Killip class, creatinine, D-dimer, peak TnT/TnI >10 ULN, peak BNP/NT-proBNP >10 ULN, ticagrelor, ACEI/ARB, beta-blockers, diuretics, and PPIs. To evaluate the linearity assumptions regarding the association between continuous LFSs and MACEs, we used restricted cubic splines. Importantly, the BARD score, a non-continuous variable, was excluded from this analysis. For all analyses, a two-tailed P-value <0.05 was considered statistically significant. The statistical analyses were performed with SPSS version 26.0 software (SPSS Inc.) and R language version 4.1.3 (Feather Spray).

            Results

            Baseline Characteristics

            Table 1 outlines the baseline characteristics according to whether the patients experienced MACEs. Patients who experienced events tended to be older; to be women and current non-smokers; to have lower BMI and diastolic blood pressure; to have higher prevalence of chronic kidney disease and a history of stroke; and to present with elevated levels of creatinine and albumin while displaying lower platelet counts. Patients with rather than without MACEs also had higher D-dimer levels, higher peak values of TnT, and peak values of BNP/NT-pro BNP. The concentrations of triglycerides, total cholesterol, and low-density lipoprotein cholesterol were lower in patients with rather than without MACEs. Meanwhile, patients with rather than without MACEs were more likely to be given clopidogrel and diuretics, but less likely to receive ticagrelor, ACEI/ARB, and beta-blockers. No statistically significant difference was observed in the number of diseased vessels or Gensini scores.

            Table 1

            Baseline Characteristics of Patients with and without MACEs.

            VariablesNon-events (n = 711)Events (n = 155)P Value
            Age, y58.50 ± 12.668.85 ± 12.150.000
            Female, n (%)139 (19.5%)48 (31.0%)0.002
            Current smokers, n (%)387 (54.4%)54 (34.8%)0.000
            Alcohol consumption, n (%)137 (19.3%)32 (20.6%)0.695
            BMI, kg/m2 25.57 ± 3.5724.28 ± 3.670.000
            Hypertension, n (%)368 (51.8%)84 (54.2%)0.582
            Diabetes mellitus, n (%)178 (25.0%)49 (31.6%)0.092
            Hyperlipidemia, n (%)191 (26.9%)41 (26.5%)0.916
            Chronic kidney disease, n (%)35 (4.9%)22 (14.2%)0.000
            Prior stroke, n (%)67 (9.4%)27 (17.4%)0.004
            Prior bleeding, n (%)46 (6.5%)16 (10.3%)0.092
            Systolic blood pressure, mmHg122.37 ± 18.95119.72 ± 19.590.118
            Diastolic blood pressure, mmHg75.05 ± 12.4570.10 ± 12.560.000
            Heart rate, bpm79.65 ± 14.6477.79 ± 16.300.162
            Platelet,109/L215 (178–252)192 (160–241)0.002
            Albumin, g/L42 (39–45)40 (37–43)0.000
            ALT, U/L34 (21–56)26 (16–44)0.000
            AST, U/L48 (23–142)44 (24–107)0.482
            Total bilirubin, umol/L15 (10.23–23.03)14 (9.02–22.56)0.289
            GGT, U/L23 (14–38)19.89 (13–33.75)0.208
            Creatinine, mg/dL0.70 (0.60–0.82)0.72 (0.64–0.90)0.000
            Triglycerides, mmol/L1.52 (1.06–2.25)1.31 (0.96–1.89)0.002
            Total cholesterol, mmol/L4.7 (3.99–5.46)4.42 (3.81–5.21)0.017
            LDL-C, mmol/L2.99 (2.43–3.57)2.80 (2.29–3.37)0.018
            HDL-C, mmol/L0.97 (0.81–1.16)1.0 (0.80–1.19)0.444
            Lp(a), mg/L106.56 (50.3–238.47)113.06 (55.02–302.86)0.323
            HbA1c, %5.5 (3.5–9.92)6.1 (4.0–10.8)0.283
            D-Dimer, mg/L0.34 (0.25–0.57)0.49 (0.30–0.95)0.000
            Peak value of TnT, ng/mL3.87 (1.69–6.81)5.42 (2.52–8.25)0.002
            Peak value of TnI, ng/mL0.93 (0.05–4.96)0.59 (0.05–5.27)0.550
            Peak TnT/TnI >10 ULN411 (57.8%)95 (61.3%)0.425
            Peak value of NT-proBNP, pg/mL1345 (664–2811.5)3056.5 (1433.3–6988)0.000
            Peak value of BNP, ng/mL155 (51.73–405.18)250 (102–869)0.005
            Peak BNP/NT-proBNP >10 ULN31 (4.4%)18 (11.6%)0.000
            LVEF, %60 (52–65)55 (48–65)0.021
            The number of diseased vessels0.084
             Single123 (77.4%)36 (22.6%)
             Multiple588 (83.2%)119 (16.8%)
            Gensini score51.5 (38.5–81.0)58.0 (40.5–83.0)0.099
            Killip class0.000
             1553 (77.8%)95 (61.3%)
             269 (9.7%)30 (19.4%)
             318 (2.5%)7 (4.5%)
             471 (10.0%)23 (14.8%)
            Aspirin, n (%)711 (100%)155 (100%)-
            Clopidogrel, n (%)484 (68.1%)118 (76.1%)0.048
            Ticagrelor, n (%)228 (32.1%)37 (23.9%)0.045
            ACEI/ARB, n (%)575 (80.9%)108 (69.7%)0.002
            Beta-blockers, n (%)668 (94.0%)138 (89.0%)0.029
            CCB, n (%)26 (3.7%)7 (4.5%)0.613
            Statins, n (%)695 (97.7%)152 (98.1%)0.808
            Diuretics, n (%)79 (11.1%)32 (20.6%)0.001
            PPI, n (%)431 (60.6%)105 (67.7%)0.098

            Continuous values are summarized as mean ± SD, median (interquartile range) and categorical variables as number (percentage).

            BMI, indicates body mass index; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, Gamma-glutamyltransferase; LDL-C, Low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; ULN, Upper limit of normal; LVEF, Left ventricular ejection fraction; ACEI, angiotensin converting enzyme inhibitors; ARB, angiotensin receptor blockers; PPI, proton pump inhibitor.

            A comparison of baseline characteristics based on the LFSs categories is presented in the Supplementary material (Tables S1 and S2). We observed no statistical difference in Gensini scores among the patients with low, intermediate, or high LFSs. As shown in Table S3, no significant correlation was observed between LFSs and the number of diseased vessels.

            Liver Fibrosis Scores Among Patients with STEMI

            Patients with rather than without MACEs had notably higher median NFS (0.559 vs. −0.387), FIB-4 (3.184 vs. 2.515), and Forns (5.943 vs. 5.018) scores. Furthermore, individuals with intermediate and high FIB-4, BARD, and Forns scores showed significantly greater risk of MACEs than those with low scores. Notably, a high-risk NFS score, rather than an intermediate score, was significantly associated with the risk of MACEs (Table 2 and Figure S1).

            Table 2

            Liver Fibrosis Scores of Patients with and without MACEs.

            Liver fibrosis scoresNon-events(n = 711)Events (n = 155)P Value
            NFS−0.387 (−1.630-1.100)0.559 (−0.923–1.901)<0.001
             Risk category0.002
             Low203 (88.3%)27 (11.7%)
             Intermediate282 (82.9%)58 (17.1%)
             High226 (76.4%)70 (23.6%)
             FIB-42.515 (1.326–5.800)3.184 (1.844–7.437)0.001
            Risk category0.002 *†
             Low165 (90.7%)17 (9.3%)
             Intermediate208 (81.3%)48 (18.8%)
             High338 (79.0%)90 (21.0%)
            BARD---
             Risk category0.001 *†
             Low119 (93.7%)8 (6.3%)
             Intermediate355 (79.6%)91 (20.4%)
             High237 (80.9%)56 (19.1%)
            Forns score5.018 (3.945–6.013)5.943 (4.986–6.838)<0.001
             Risk category<0.001 *†‡
             Low216 (90.8%)22 (9.2%)
             Intermediate404 (81.1%)94 (18.9%)
             High78 (68.4%)36 (31.6%)

            NFS indicates nonalcoholic fatty liver disease fibrosis score; FIB-4, fibrosis-4; BARD, body mass index, AST/ALT ratio, diabetes mellitus score.

            *P < 0.0167, Low risk group vs Intermediate risk group; †P < 0.0167, Low risk group vs High risk group; ‡P < 0.0167, Intermediate risk group vs High risk group.

            Association Between LFSs and MACEs, According to Univariable and Multivariable Analysis

            The median follow-up duration was 4 years. Throughout this period, a total of 155 MACEs were observed, including 52 all-cause deaths, 59 nonfatal MIs, 28 ischemic strokes, and 16 ALI. Kaplan-Meier analysis of MACEs demonstrated that patients with higher rather than lower NFS scores had significantly lower event-free survival rates (Figure 2). Patients with intermediate to high-risk FIB-4 and BARD scores had notably lower event-free survival rates than patients with low-risk scores. Pairwise comparison of MACEs revealed statistically significant differences among the three groups of patients, grouped according to Forns score (Figure 2).

            Figure 2

            Cumulative Event-Free Survival Analysis for MACEs According to Liver Fibrosis Scores at Baseline.

            (A) NFS; (B) FIB-4; (C) BARD; (D) Forns score. NFS, nonalcoholic fatty liver disease fibrosis score; FIB-4, fibrosis-4; BARD, body mass index, AST/ALT ratio, and diabetes mellitus score. #P < 0.0167, low risk group vs intermediate risk group; *P < 0.0167, low risk group vs high risk group; &P < 0.0167, intermediate risk group vs high risk group.

            Table S4 demonstrates the independent risk factors associated with MACEs. In the multivariable analysis, a high FIB-4 score (HR = 1.922; 95% CI, 1.085–3.405), intermediate or high BARD score (HR = 2.597; 95% CI, 1.242–5.429; HR = 2.395; 95% CI, 1.115–5.142), and high Forns score (HR = 2.271; 95% CI, 1.250–4.125) were significantly associated with MACEs, whereas the NFS score showed no association. The incidence of MACEs increased 28.5% with each 1 SD increase in Forns score. We observed no associations between the NFS, FIB-4, or BARD score and the risk of MACEs (Table 3).

            Table 3

            Adjusted Hazard Ratios of MACEs According to Different Liver Fibrosis Scores Among STEMI Patients.

            ScoreAdjusted Hazard Ratio (95% CI)P Value
            NFS
             Per 1-SD1.069 (0.988–1.157)0.098
             Low riskRef
             Intermediate risk1.101 (0.680–1.784)0.696
             High risk1.343 (0.822–2.197)0.239
            FIB-4
             Per 1-SD1.003 (0.972–1.035)0.865
             Low riskRef
             Intermediate risk1.789 (0.977–3.275)0.060
             High risk1.922 (1.085–3.405)0.025
            BARD
             Per 1-point1.169 (0.952–1.435)0.139
             Low riskRef
             Intermediate risk2.597 (1.242–5.429)0.011
             High risk2.395 (1.115–5.142)0.025
            Forns
             Per 1-SD1.285 (1.139–1.450)0.000
             Low riskRef
             Intermediate risk1.585 (0.957–2.623)0.073
             High risk2.271 (1.250–4.125)0.007

            The adjusted model included sex, current smoking, prior stroke, chronic kidney disease, diastolic blood pressure, LVEF, Killip class, creatinine, D-Dimer, peak TnT/TnI >10 ULN, peak BNP/NT-proBNP>10 ULN, baseline use of ticagrelor, ACEI/ARB, Beta-blockers, diuretics, PPI, other than the variables included in the score formula.

            NFS indicates nonalcoholic fatty liver disease fibrosis score; FIB-4, fibrosis-4; BARD, body mass index, AST/ALT ratio, diabetes mellitus score; LVEF, left ventricular ejection fraction; ULN, Upper limit of normal; ACEI, angiotensin converting enzyme inhibitors; ARB, angiotensin receptor blockers; PPI, proton pump inhibitor.

            Predictive Value of LFSs for MACEs

            We assessed whether the LFSs might improve risk stratification for MACEs, on the basis of ROC curve analysis. The performance of the basic model in predicting MACEs showed moderate improvements over the GRACE score (0.7181 vs. 0.7072). Additionally, the incorporation of LFSs into the basic model demonstrated a non-significant enhancement in the predictive capability for MACEs. The Forns score combined with the basic model probably had the best predictive value for MACEs (AUC = 0.7376) (Figure 3). However, addition of the LFs to the basic model did not yield a statistically significant difference in the change in the ROC curve. Restricted cubic splines analysis exhibited a notable tendency toward a nonlinear association of the continuous NFS, FIB-4, and Forns scores with the HR of MACEs (Figure 4).

            Figure 3

            ROC for MACEs According to Different LFS Models.

            The basic model included sex, current smoking, prior stroke, chronic kidney disease, diastolic blood pressure, LVEF, Killip class, creatinine, D-dimer, peak TnT/TnI >10 ULN, peak BNP/NT-proBNP >10 ULN, baseline use of ticagrelor, ACEI/ARB, beta-blockers, diuretics, and PPIs. NFS, nonalcoholic fatty liver disease fibrosis score; FIB-4, fibrosis-4; BARD, body mass index, AST/ALT ratio, diabetes mellitus score; LVEF, left ventricular ejection fraction; ULN, upper limit of normal; ACEI, angiotensin converting enzyme inhibitors; ARB, angiotensin receptor blockers; PPI, proton pump inhibitor.

            Figure 4

            Restricted Cubic Spline Plot of Liver Fibrosis Scores and Risk of MACEs.

            (A) NFS; (B) FIB-4; (C) Forns score. NFS, nonalcoholic fatty liver disease fibrosis score; FIB-4, fibrosis-4.

            Subgroup Analysis and Sensitivity Analysis Between LFSs and MACEs

            Subgroup analysis was performed by age (<65 or ≥65 years), sex, diabetes, BMI (<28 kg/m2 or ≥28 kg/m2), and diseased vessels (single or multiple-vessel disease). No significant differences were observed between subgroups except for age (Table S5). A comparison of patients younger than 65 years versus 65 years or older indicated a statistically significant higher HR for Forns in the younger age group (P for interaction = 0.030).

            Overall, 180 patients (48.4%) were diagnosed with NAFLD among the 372 patients who underwent abdominal ultrasound or CT. Our comparison of baseline characteristics revealed no statistically significant differences between patients with versus without imaging, except for total bilirubin and gamma-glutamyltransferase, as illustrated in Table S6. Elevated LFSs were significantly associated with clinical outcomes among patients with NAFLD and without NAFLD (all P values for interaction <0.05, Table S7).

            Discussion

            Our findings indicated a significant association between elevated baseline LFSs, encompassing NFS, FIB-4, BARD, and Forns, and the risk of long-term cardiovascular outcomes in this single center study of patients with STEMI. Moreover, adding the LFSs into the basic model moderately improved the ability to predict MACEs over that of the GRACE score. Among the four LFSs, the Forns score had the best predictive value for MACEs. This study offers novel insights into using LFSs to identify patients at high-risk of STEMI with poor prognostic outcomes.

            Multiple studies have provided compelling evidence indicating a robust association between NAFLD and CVD [14]. The presence of advanced fibrosis, evaluated with noninvasive markers, has been associated with atherosclerosis [15, 16] and the calcification of coronary arteries [1719]. Patients with rather than without NAFLD have been found to have a higher incidence of plaque progression, as determined by coronary CT angiography [20, 21]. Elevated oxidative stress, indicated by oxidized LDL, is considered a potential mediator linking NAFLD to high-risk coronary plaques [22]. Furthermore, patients with NAFLD tend to exhibit elevated prevalence of coronary microvascular dysfunction and impaired myocardial perfusion [23, 24]. In our study, no significant association was observed between liver fibrosis scores and Geniss scores. However, we were unable to evaluate vulnerable and high-risk coronary artery plaques or coronary microvascular dysfunction.

            Noninvasive liver fibrosis score systems can be used as a risk stratification tool for predicting cardiovascular outcomes in patients with CAD. In a cohort of 5143 patients with angiography-demonstrated stable CAD, both NFS and FIB-4 have been found to show independent associations with cardiovascular mortality, non-fatal MI, and stroke [8]. An analysis of 3263 patients with acute coronary syndrome and stable CAD has highlighted that elevated liver fibrosis scores independently correlate with heightened risk of all-cause and cardiovascular mortality [25]. Moreover, among individuals with a history of MI, liver fibrosis scores have emerged as independent predictors of MACEs [7]. Similarly, an association between liver fibrosis and MACEs has been observed in individuals with stable CAD undergoing PCI [9]. Comparisons of LFSs for predicting prognosis are limited and have yielded conflicting findings. In patients with previous MI and stable CAD undergoing PCI, a 1-SD increase in the NFS score has been reported to correspond to a 1.28- to 1.59-fold higher risk of adverse outcomes [7, 9]. In contrast, the NFS score was not found to contribute to the increase in MACEs after multivariable adjustment among patients with STEMI in our study. The correlation between the Forns score and MACEs has been observed not only in patients with prior MI [7] but also in those with acute coronary syndrome and stable CAD [19]. Our study indicated that the Forns score was significantly associated with MACEs among patients with STEMI, whereas such an association has not been detected in patients with stable CAD after elective PCI. [9] Future studies must be conducted to identify the appropriate risk markers in patients with stable and unstable CAD.

            Recent studies have highlighted the potential therapeutic roles of sodium-glucose cotransporter 2 (SGLT-2) inhibitors and glucagon-like peptide 1 receptor agonists (GLP-1 RAs) in preventing the progression from NAFLD to liver fibrosis [26, 27]. We postulated that patients with high LFSs, a marker of metabolic disorders, might be considered for treatment with metabolic modulators demonstrated to improve cardiovascular outcomes, for example, GLP-1 RAs or SGLT2 inhibitors. Studies have indicated the effectiveness of GLP-1 RAs in improving hepatic fat content, liver biochemistry, and inflammatory markers [28] GLP-1 RAs have shown efficacy in addressing hepatic steatosis and inflammation, although the effects on fibrosis markers did not reach statistical significance [28]. A phase 2 trial in patients with NASH has indicated a notably higher resolution percentage after semaglutide treatment [29]. SGLT-2 inhibitors have shown substantial promise in attenuating inflammation and fibrogenesis, while improving organ function [3032]. A meta-analysis of data from 12 RCTs has underscored the considerable decreases in serum ALT, GGT levels, and liver fat content, measured via MRI, after treatment with SGLT-2 inhibitors [33]. Recent RCTs have further highlighted the potential benefits of SGLT-2 inhibitors in individuals with NAFLD and NASH, as well as obesity and type 2 diabetes, through the imaging assessment of hepatic steatosis with MRI-based techniques [34].

            This study has several limitations that must be noted. First, we were unable to assess changes in LFSs during the follow-up. The progression of liver fibrosis might have worsened, thus potentially causing misclassification. Second, transient elevation of ALT and AST was observed in some patients with acute MI. Liver fibrosis might have been over-estimated according to the NFS, FIB-4, and BARD scores. However, the Forns score was better than NFS, FIB-4, and BARD scores for predicting prognosis in patients with acute MI. That finding might be partially explained by the Forns score’s basis on platelet count, GGT, and total cholesterol, which were unlikely to have been influenced by myocardial infarction and hemodynamic disorder. Third, NAFLD data were available for fewer than half of the patients. However, a comparison of baseline characteristics between patients with versus without NAFLD confirmed via imaging indicated no statistically significant differences, except for total bilirubin and gamma-glutamyltransferase levels. Forth, we cannot fully rule out potential effects of undetected liver diseases on the LFSs in this study. Fifth, we did not collect data on the use of drug balloons and thrombus aspiration. Finally, the study’s retrospective design, without a balanced rate of risk factors, and with a single center design and small sample size, might limit the generalizability of the study findings.

            Conclusion

            In this cohort of patients with STEMI, our findings revealed that approximately two-thirds of individuals exhibited moderate to high levels of LFSs. LFSs were significantly associated with elevated risk of long-term outcomes. However, further prospective studies with extended follow-up periods and larger sample sizes are warranted to explore the mechanisms involved.

            Data Availability Statement

            The datasets used and/or analyzed during the current study will be made available by the corresponding author upon reasonable request.

            Ethics Statement

            The study protocol was approved by the Ethics Committee of the China-Japan Friendship Hospital. This cohort study was conducted according to the principles of the Declaration of Helsinki. Informed consent was obtained from all participants and/or their legal guardian(s).

            Authors’ Contributions

            Yihong Sun conceived and designed the study. Longyang Zhu analyzed and interpreted the data, and drafted the manuscript. Qing Li, Yinong Chen, Zhe Wang, and Siqi Jiao were involved in the review of the manuscript. Shuwen Zheng and Furong Yang collected relevant data regarding the incidence of NAFLD among the STEMI population. All authors read and approved the final manuscript.

            Acknowledgments

            The authors acknowledge financial and moral support from the Capital Health Research and Development of Special, and National High-Level Hospital Clinical Research Funding, and thank the patients for participating in this research.

            Conflicts of Interest

            The authors declare no conflicts of interest.

            Supplementary material

            Supplementary material for this paper can be found at https://cvia-journal.org/wp-content/uploads/2024/01/SUPPLEMENTAL_MATERIAL-2.pdf.

            Citation Information

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            Author and article information

            Journal
            CVIA
            Cardiovascular Innovations and Applications
            CVIA
            Compuscript (Ireland )
            2009-8782
            2009-8618
            24 January 2024
            : 9
            : 1
            : e991
            Affiliations
            [1] 1Department of Cardiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing 100029, China
            [2] 2Department of Geriatrics. National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
            [3] 3Peking Union Medical College, China-Japan Friendship Hospital, Beijing 100029, China
            [4] 4Department of Cardiology, Beijing University of Chinese Medicine, Beijing 100029, China
            [5] 5Department of Cardiology, China–Japan Friendship Hospital, Beijing 100029, China
            Author notes
            Correspondence: Yihong Sun, Peking University China-Japan Friendship School of Clinical Medicine, Department of Cardiology, Peking University, China-Japan Friendship School of Clinical Medicine, No. 2 East Yinghua Road, Chaoyang District, Beijing 100029, China, E-mail: yihongsun72@ 123456163.com
            Article
            cvia.2023.0095
            10.15212/CVIA.2023.0095
            c3709451-8daf-4611-90ea-686b649549a6
            Copyright © 2024 Cardiovascular Innovations and Applications

            This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 Unported License (CC BY-NC 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See https://creativecommons.org/licenses/by-nc/4.0/.

            History
            : 09 August 2023
            : 07 November 2023
            : 10 December 2023
            Page count
            Figures: 4, Tables: 3, References: 34, Pages: 12
            Funding
            Funded by: Capital Health Research and Development of Special
            Award ID: 2020-2-4065
            Funded by: National High-Level Hospital Clinical Research Funding
            Award ID: 2022-NHHCRF-PY-19
            This work was supported by the Capital Health Research and Development of Special (2020-2-4065) and National High-Level Hospital Clinical Research Funding (2022-NHHCRF-PY-19).
            Categories
            Research Article

            General medicine,Medicine,Geriatric medicine,Transplantation,Cardiovascular Medicine,Anesthesiology & Pain management
            major cardiovascular events,ST-segment elevation myocardial infarction,liver fibrosis scores

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