Evaluation of MASLD Fibrosis, FIB-4 and APRI Score in MASLD Combined w | IJGM

Introduction

With the improvement of living standards, diabetes mellitus has become one of the most common chronic diseases in the world widely.¹ T2DM (Type 2 diabetes mellitus) is closely associated with cardiovascular diseases (CVD), including atherosclerosis, hypertension and heart failure.² CVD is the main cause of disability and disease burden globally, leading to premature mortality and increased health care costs.³

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly referred to as non-alcoholic fatty liver disease (NAFLD), is recognized globally as a significant cause of chronic liver disease. The diagnosis of MASLD includes evidence of liver steatosis and at least one of the following five cardiac metabolic criteria: overweight or obesity, impaired glucose regulation or T2DM, hypertension, increased plasma triglycerides (TG) or decreased high-density lipoprotein cholesterol (HDL-c).⁴ T2DM has long been recognized as an independent cardiovascular risk factor and is a common finding in MASLD. Approximately 60% of patients with T2DM have MASLD.^5,6 Furthermore, patients with T2DM combined with MASLD had a higher risk of CVD compared with patients with T2DM alone. This suggests a potential synergistic effect on cardiovascular disease occurrence in patients.^7,8 In the majority of patients, MASLD is closely associated with insulin resistance, hypertension, atherogenic dyslipidaemia, dysglycaemia and being overweight or obese, which are all established risk factors for CVD.^9–11 Conversely, previous studies have shown that the main cause of death in MASLD patients is CVD.¹² In addition, it is estimated that 20–30% of patients with MASLD will progress to liver inflammation and fibrosis.¹³

Liver fibrosis, caused by abnormal liver fat metabolism, may lead to abnormal accumulation of liver fat, resulting in inflammation, glucose metabolism disorder and also eventually cause CVD occurrence to increase.¹⁴ So, can fibrosis indicators predict the occurrence of MACCEs in clinic?

The gold standard for diagnosing liver fibrosis in MASLD is liver biopsy. The main limitations of liver biopsy are its invasiveness, high incidence of possibility of complications, sampling variability, and increased costs.¹⁵ FIB-4 is a highly sensitive biomarker for the assessment of advanced liver fibrosis. Recent studies have focused on the predictive value of FIB-4 in the development and prognosis of CVD.^16,17

Sodium glucose co-transporter-2 inhibitors (SGLT2i) is developed as an anti-diabetic drug. SGLT2i significantly reduced the cardiovascular and all-cause mortality rates, as well as the risk of hospitalization for heart failure in T2DM patients with established cardiovascular diseases and/or cardiovascular risk factors.^18–20 SGLT2i has shown some liver protective effects including reducing serum transaminase levels and improving hepatic fat content.²¹ Whereas the beneficial effect of SGLT2i on liver fibrosis in patients, especially those combined with MACCEs has not yet been reported.^22,23

The aim of this study was to investigate the predictive value of fibrosis index, FIB-4 score for MACCEs in MASLD combined with T2DM receiving SGLT2i treatment.

Material and Methods

Study Design

In this case-cohort study, we reviewed the records of 280 patients who visited and completed MASLD and T2DM screening in First Affiliated Hospital of Xinjiang Medical University from January 2014 to October 2023. Patients with incomplete data in our method have been excluded from the analysis. We have excluded lack of information (n = 27), No use of blood glucose-lowering medication (n = 10) without affecting the overall analysis. The flowchart is shown in Figure 1. Clinical baseline data, laboratory data, and abdominal ultrasound results were collected from all study subjects during their first visit and hospital follow-up. MACCEs were considered as the endpoints.

MASLD was diagnosed by experienced clinicians, with following criteria met: (1) fat deposition on imaging modalities, such as ultrasonography, computed tomography, and magnetic resonance imaging; (2) week alcohol consumption 24,25 T2DM was diagnosed based on plasma glucose criteria, either fasting plasma glucose (FBS) or 2h plasma glucose during a 75g oral glucose tolerance test or hemoglobin A1c (HbA1c) criteria.²⁶

MACCEs include cardiac and noncardiac deaths, nonfatal acute myocardial infarction, unplanned revascularization (new percutaneous coronary intervention or cardiac bypass surgery), malignant arrhythmia, congestive heart failure, and stroke.^27–29 We defined dyslipidemia according to the Chinese guidelines for the prevention and treatment of T2DM (2020) as a manifestation of the following four indicators: TC ≥4.5 mmol/L, TG ≥1.7 mmol/L, HDL-C 30 Hypertension was defined as the manifestation of systolic blood pressure (SBP) ≥140 mmHg or diastolic blood pressure (DBP) ≥90 mmHg or hypertension was controlled with medication on. Blood pressures (BP) were measured on three different days in the absence of antihypertensive drugs after 15 minutes of rest, and the average BP was calculated.³¹

Exclusion criteria: patients with incomplete data; consumed alcohol ≥30 g/day; any cancer, rheumatic mitral valve disease and autoimmune disease. In addition, long-term efficacy was assessed at the time of analysis in MASLD and T2DM treated with SGLT2i. The study was conducted in accordance with the guidelines of the Declaration of Helsinki. The protocol was approved by the Ethics Committee of First Affiliated Hospital of Xinjiang Medical University. Written informed consent was obtained from the patient. The ethical approval number for the study is K202306-14.

Clinical and Laboratory Data

Clinical and laboratory data were recorded during the study period. Body mass index (BMI) was calculated as weight (kg) divided by height in meters squared (m²). Blood samples were collected by venipuncture after an overnight fast. Laboratory assessments included HbA1c, fasting plasma glucose (FPG), creatinine, total cholesterol (TC), triglyceride, HDL-C, low-density lipoprotein cholesterol (LDL-C), aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP) and creatinine (Cr). All the laboratories’ biochemical indicators were measured by the Dimension AR/AVL Clinical Chemistry System (Newark, NJ, USA) in the central laboratory. FIB-4 index was calculated using the formula: Age (years)×AST (U/L)/[PLT (10⁹/L) ×√ALT (U/L).³² APRI index was calculated using the following formula: (AST (U/L)/upper-limit of normal)/platelet count (10⁹/L) ×100).³³

Statistical Analysis

All data analyses were performed in the SPSS version 26.0 statistical software package (Chicago, IL, USA). Normally distributed data were expressed as the mean ± SD and analyzed using a t-test. Non-normally distributed data were expressed using the median with interquartile range (IQR) and analyzed by the rank-sum test. Categorical variables were presented as frequencies and percentages and compared using the Chi-square or Fisher’s test. Multivariate logistic factors were employed to analyze the correlation between each variable and the occurrence of MACCEs. The level of statistical significance was set at P

Results

General Clinical Characteristics

The baseline characteristics of 280 MASLD patients with T2DM before 24 weeks of treatment with or without SGLT2-I in Table 1. There were 178 males and 102 females, with a median age of 63 ± 12 years and a median HbA1c level of 7.7% (IQR, 6.6%–8.83%). Prior to the start of the observation, 41 patients (14.5%) smoked, 58 patients (20.5%) drank, 97 patients (35.0%) had Obesity ≥28, 202 patients (72.1%) had Hypertension, 134 patients (47.9%) had CAD, 118 patients (42.1%) had MACCEs, and 24 patients (8.6%) had Dyslipidemia. All patients used hypoglycemic drugs including insulin (n = 149), metformin (n = 197), dipeptidyl peptidase-4 inhibitors (n = 57), and GLP-1 receptor inhibitors (n = 64).

Table 1 Demographic and Clinical Characteristics of the MASLD Type 2 Diabetes Cohort Before and After SGLT2i Use

All patients used non-diabetes drugs, including Aspirins (n = 154), Statins † (n = 170), Fibrates † (n = 11). The median of FIB-4 index is 1.42 (IQR, 1.00–2.00). The median of APRI index is 0.29 (IQR, 0.21–0.38).

Clinical Characteristics of SGLT2i Before and After Treatment

The median follow-up time was 22 months (interquartile range, 15–35 months). In the non-SGLT2i group, the data showed a significant increase in FIB-4 levels compared with baseline (mean 1.44 vs 1.59, P=0.014). FIB-4 and APRI were significantly reduced in the SGLT2i group compared with the non-SGLT2i group. Patients treated with SGLT2i, showed significant improvement in FPG (mean 7.67 vs 6.62 mmol/L P=0.001), and APRI level [0.30 (0.22–0.52) vs 0.28 (0.20–0.36), P=0.014]. FIB-4 level after treatment was significantly lower compared to baseline [1.40 (1.01–2.03) vs 1.25 (0.94–1.73), P=0.03, Table 1].

Demographic and Clinical Characteristics of Patients with MACCEs

We then subdivided the patients into two groups according to the occurrence of MACCEs. The patients with MACCEs were further divided into two groups according to SGLT2i treatment or not (Table 2). Compared to non-SGLT2i treatment patients, those with SGLT2i use tended to be younger (66 ± 10 vs 61 ± 9 years, P=0.006), and APRI score was significantly decreased [0.34 (0.22–0.44) vs 0.27 (0.20–0.34), P=0.04], and lower FIB-4 [1.83 (1.24–2.40) vs 1.35 (0.96–1.77), P=0.003].

Table 2 Demographic and Clinical Characteristics in a Cohort of All Occurrence MACCEs of MASLD Type 2 Diabetes Patients Non-Use and Use SGLT2i

Occurrence of MACCEs

In those MASLD patients combined with T2DM with SGLT2i non-treatment tended to have more occurrence of MACCEs [66 (45.5%) vs 52 (38.5%), P=0.28] and more CVA (cerebrovascular accident) patients [52 (35.9%) vs 31 (22.5%), P=0.018] compared to those SGLT2i use patients (Table 3). We then analyzed the difference between subgroups stratified by sex, hypertension, FIB-4, and dyslipidemia. As shown in Figure 2, multivariate logistic regression analysis showed that SGLT2i treatment (OR: 0.76, 95% CI 0.46–1.26; P=0.28), hypertension (OR: 2.06, 95% CI 1.17–3.64; P=0.01) affect the occurrence of MACCEs. In another model, we found that SGLT2i treatment significantly reduced liver fibrosis indicators (FIB P=0.04, Figure 3).

Table 3 Outcomes of MACCES in Cases (SGLT2i Users) and Controls (Non-SGLT2i Users) in a Cohort of Patients with MASLD and Type 2 Diabetes

Figure 2 Logistic regression analysis of MACCEs risk factors forest map in patients with MASLD combined with T2DM. Abbreviations: OR, Odds ratio; CI, Confidence interval; SGLT2i, Sodium glucose co-transporter-2 inhibitors.

Figure 3 Logistic regression analysis of liver fibrosis (FIB-4) risk factors forest map in patients with MASLD combined with T2DM. Abbreviations: OR, Odds ratio; CI, Confidence interval; SGLT2i, Sodium glucose co-transporter-2 inhibitors; MACCEs, major adverse cardiovascular and cerebrovascular events.

Mortality

There was no significant difference in all-cause mortality between the two groups (38.5% vs 45.5%, P=0.28). All-cause death (3.38% vs 0.7%) and cardiac death (2.7% vs 0.7%) in SGLT2i use group decreased sharply compared to those in the non-SGLT2i use group (Table 3).

Effect of SGLT2i on the Fibrosis Status Stratified by FBI-4 Index

We also categorized participants into 3 groups based on the FIB-4 index values after treatment: FIB-4 2.67, and FIB-4 index ≥2.67, which corresponded to low, intermediate, and high risk of advanced fibrosis.³⁴ Among all patients treated with SGLT2i, the median FIB-4 index decreased from 1.40 at baseline to 1.24 after SGLT2i treatment (P=0.045; Figure 4A) at 22 months (interquartile range, 15−35 months). In the low-risk group, the median level decreased from 0.98 to 0.95 after SGLT2i treatment (P=0.039; Figure 4B). In the intermediate-risk group, the median level decreased from 1.93 to 1.69 after SGLT2i treatment (P=0.035; Figure 4C). In the high-risk group, the median level decreased from 3.24 at baseline to 3.05, although the difference was not statistically significant (P=0.89; Figure 4D).

Figure 4 Effects of SGLT2i treatment on FBI-4 index change. Changes in the median of group from baseline to ≥ 24 weeks in all patients (A), group FIB-4 index B), intermediate-risk group (1.3 ≤FIB-4 index C), and high-risk group (2.67≥FIB-4 index) (D), (*P

As shown in Figure 5, all of the patients with MACCEs occurrence were then subdivided into three groups according to FIB-4 index. We found that the number of patients treated with non-SGLT2i was higher than the number of patients treated with SGLT2i, in the low-risk group low, intermediate, and high-risk group.

Figure 5 Effects of SGLT2i treatment on MACCEs occurrence. The number of patients in the fibrosis risk group treated with SGLT2i versus the number of patients not treated with SGLT2i who developed MACCEs in all patients (A), group FIB-4 index B), intermediate-risk group (1.3 ≤FIB-4 index C), and high-risk group (2.67≥FIB-4 index) (D).

We observed changes in the number of MACEEs occurring with and without SGLT2i drugs in the FIB-4 subgroup. We found that the number of patients in the FIB-4 low-risk group treated with SGLT2i use had more MACCEs than those untreated with SGLT2i [25 (36.23%) vs 20 (38.46%)]. In intermediate- risk and high-risk groups, the number of MACCEs occurred in fewer people after treatment with SGLT2i than without SGLT2i treatment (Figure 5).

Figure 6 shows the change in the proportion of fibrosis risk group in MASLD patients combined with T2DM at baseline and after treatment. Overall, the proportion of patients in the low-risk group increased from 45.9% (62 of 135) to 51.9% (70 of 135) after treatment with SGLT2i, the proportion of patients in the intermediate-risk groups decreased from 46.7% (63 of 135) to 43.0% (58 of 135), whereas that of the high-risk group decreased from 7.4% (10 of 135) to 5.2% (7 of 135) (Figure 6A). Among 62 patients in the low-risk group, 41 (66.13%) remained at low-risk, and 20 (32.26%) changed to intermediate-risk, and 1 (1.61%) changed to high-risk after treatment with SGLT2i (Figure 6B). Among the 63 patients in the intermediate-risk group, 27 (42.86%) improved to low-risk; 33 (52.38%) remained at intermediate-risk; and 3 (4.76%) changed to high-risk after treatment with SGLT2i (Figure 6C). Among the 10 patients in the high-risk group, 2 (20%) improved to low-risk, 5 (50%) improved to intermediate-risk; and 3 (30%) remained at high -risk after treatment with SGLT2i (Figure 6D).

Figure 6 Changes in the proportion of fibrosis risk groups at baseline and after SGLT2i treatment in patients with MASLD combined with T2DM. Changes in the proportion of group from baseline to ≥ 24 weeks in all patients (A), group FIB-4 index B), intermediate-risk group (1.3 ≤FIB-4 index C), and high-risk group (2.67≥FIB-4 index) (D).

Discussion

In this study, we aimed to investigate the fibrosis scores, FIB-4 in MASLD patients combined with T2DM and MACCEs receiving SGLT2i treatment. In our current study, at the beginning of SGLT2i therapy, 46.67% of T2DM patients with liver fibrosis were in the intermediate-risk group and 7.4% suffered from it in the high-risk group. After 22 months of treatment, 42.96% of patients were in the intermediate-risk group and 5.18% in the high-risk group. This indicates that some patients showed improvement in liver fibrosis after SGLT2i treatment. Patients treated with SGLT2i have fewer MACCEs occurrence, and we found for the first time that MASLD fibrosis index, FIB-4 was improved in MASLD patients combined with T2DM and MACCEs receiving SGLT2i treatment.

MASLD, as a chronic disease, has become an important health issue.35 The prevalence of MASLD is significantly increased in patients with T2DM,^36–39 and the progression is closely related to insulin resistance.⁴⁰ MASLD combined T2DM with further worsens glucose metabolism and increases the incidence rate of NASH.⁴¹ The coexistence of glucose and fat toxicity has greatly increased the incidence of macrovascular events in diabetes and accelerated the transformation of fatty liver to cirrhosis and even cancer.⁴²

The extrahepatic complications besides MASLD, such as T2DM, dyslipidemia, and cardiovascular disease, causing a tremendous clinical and economic burden.^43–45 It has been determined that the main cause of death in MASLD patients is CVD.¹² Recently, Sinn et al showed that MASLD also contributed to the incidence of myocardial infarction.⁴⁶ CVD is the main cause of death in patients with T2DM, accounting for two-thirds of all deaths,⁴⁷ and the increasing HbA1c levels are closely related to increased risk of heart disease and overall mortality.⁴⁸ Insulin resistance and impaired insulin signal transduction can also affect atherosclerosis pathogenesis and enhance progression of atherosclerosis and plaque vulnerability.⁴⁹ In addition, it is well known that the risk factors of MACCEs include age, diabetes, hypertension, dyslipidemia and the severity of ischemic disease.⁵⁰ CVD such as ischemic heart disease and stroke are also major global health problems, as they are the main causes of global mortality. Targher et al found that MASLD is associated with a higher risk of fatal and/or non-fatal CVD events, and the greater the severity of MASLD, the further the risk will increase,⁵¹ possibly due to fibrosis stage, steatosis grade or oxidative stress.⁵² Jacqueline et al found that in adults with biopsy-proven MASLD, advanced fibrosis on biopsy and higher MASLD fibrosis score were significant and independent predictors of incident cardiovascular disease.⁵³

Initially, the FIB-4 index was used to test liver injury and liver fibrosis in MASLD. The FIB-4 index consists of four parameters (age, AST, ALT and platelets) and is a simple, inexpensive and accurate tool.⁵⁴ The 2023 ADA guidelines recommend that all adult patients with T2DM or prediabetes, especially those with obesity or cardiovascular-metabolic risk factors/diagnosed cardiovascular disease, should be screened for MASLD using the FIB-4 index score even if their plasma aminotransferases are normal (perform MASLD screening/fibrosis risk stratification) screening/fibrosis risk stratification.⁵⁵ Recent studies have reported that the FIB-4 index value predicted not only future liver-related events but also future extrahepatic cancers and major adverse cardiovascular events.⁵⁶ Beomseok et al, found that high FIB-4 is a highly predictive risk factor for hepatic cell carcinoma incidence among Korean HBsAg carriers.⁵⁷

It has been shown that in animals, SGLT2i treatment reduces liver weight and serum ALT levels in streptozocin-treated mice fed a high fat diet.⁵⁸ SGLT2i also has an impact on the prevention of heart failure and cardiovascular death in patients with T2DM at high cardiovascular risk.⁵⁹ Arai’s study found that after more than 48 weeks of SGLT2i treatment in 202 MASLD patients, there was a significant decrease in body weight, liver transaminase, plasma glucose, HbA1c, and FIB-4 index.²⁴ Alfredo et al’s study found that in patients affected by T2DM, the combination of empagliflozin + metformin vs metformin monotherapy ameliorated liver steatosis, ALT levels, body weight, and glycated hemoglobin after a 6-month follow-up.⁶⁰

Recently, studies have shown a bidirectional relationship between NAFLD and hypertension. NAFLD is an important risk factor for hypertension, and hypertension may independently promote the development of fatty liver disease. The pathological and physiological mechanisms leading to this interaction seem to be insulin resistance, RAAS, and activation of the sympathetic nervous system.⁶¹ Although Ang II appears to be the primary hormone promoting cardiac fibrosis in hypertensive heart disease, the RAAS system demonstrates profibrotic activity. Targeted treatment of the RAAS pathway may help slowdown the progression of fibrosis in hypertensive heart disease and liver fibrosis. In human and animal studies, SGLT2i can enhance the antihypertensive effect of angiotensin receptor blockers.⁶² Therefore, the SGLT2i induced elevated Ang II levels could act through AT2R resulting in vasodilation, sodium excretion, antiproliferative and anti-inflammation effects [63]. Therefore, SGLT2i may counter Ang II signalling, which acts as a potent fibrosis signalling agent locally in the liver.

We found that the fibrosis status significantly was improved after treatment with SGLT2i, especially in those patients with mild to moderate fibrosis (according to FIB4 value). So, we speculate that SGLT2i may be one of the promising options for mild to moderate NASH patients, but it still requires extensive clinical research to confirm. In the future, further studies will be conducted to investigate the effects of SGLT2i treatment, combined with GLP-1R, and long-term therapy on liver fibrosis progression, cardiovascular outcomes, and overall mortality in patients with MASLD and T2DM. Previous studies have shown that high fibrosis scores are associated with an increased risk of cardiovascular disease. However, few studies have explored the relationship between liver fibrosis and MACCEs. Notably, we found that SGLT2i significantly reduced the FIB-4 index in intermediate-risk high-risk populations with a FIB-4 index ≥1.3, and fewer MACCEs occurred in SGLT2i-treated patients than in non-SGLT2i-treated patients, suggesting that SGLT2i may have a favorable effect on liver fibrosis in patients at risk for liver fibrosis progression. Therefore, these data suggest a potential beneficial role of SGLT2i in terms of MACCEs in patients with T2DM and MASLD.

Limitations

This study has several limitations. First, we performed a retrospective case–control study, which is prone to selection bias thereby limiting the validity of the results presented. In addition, lifestyle and dietary habits were potential confounding factors that were not systematically recorded during the study period. Second, the relatively small sample size constrains the robustness of multivariate analyses. Third, due to the nature of the retrospective study, liver biopsy was not performed to diagnose MASLD or assess liver fibrosis. Fourth, the potential effects of medications other than SGLT2i (eg, DDP-4 inhibitors) were not addressed in our analysis. Despite these, the FIB-4 index is a simple, accurate, and inexpensive method of assessing liver fibrosis, and its measurement is recommended. Our study found that patients treated with SGLT2i were less likely to have MACCEs with liver fibrosis index FIB-4 improvement. The generalizability of the findings may be limited to the specific patient population studied.

Conclusion

In conclusion, we proved that SGLT2i treatment could reduce MACCEs occurred in MASLD combined with T2DM; SGLT2i can alleviate the fibrosis status, especially those MASLD patients with low to intermediate risk of liver fibrosis; and the NASH fibrosis index FIB-4 may be a potential surrogate for the occurrence of MACCEs in MASLD combined with T2DM.

Ethics Approval and Consent to Participate

The study was conducted according to the standards of the Declaration of Helsinki, and its experimental protocols were approved by the Ethics Committee of First Affiliated Hospital of Xinjiang Medical University. Written informed consent was obtained from all subjects and/or their legal guardian(s). All participants consented for drawing their blood samples and collection of their relevant clinical data.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation, or all these areas, and took part in drafting, revising, or critically reviewing the article. All authors gave final approval of the version to be published, have agreed on the journal to which the article has been submitted and are accountable for all aspects of the work.

Funding

The survey was funded by the Xinjiang Young Scientific and Technical Talents Training Project (2019Q040), Natural Science Foundation of Xinjiang Uygur Autonomous Region, Outstanding Youth Science Foundation Project (2021D01E28), the National Natural Science Foundation of China (81960078), State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Xinjiang Medical University (SKL-HIDCA-2021-XXG4), Xinjiang Youth Science and Technology Top Talents Special Project (2022TSYCCX0103) and Xinjiang Science and Technology Innovation Team (Tianshan Innovation Team, 2022TSYCT-D0014).

Disclosure

The authors declare no competing interests in this work.

References

1. Bommer C, Heesemann E, Sagalova V, et al. The global economic burden of diabetes in adults aged 20-79 years: a cost-of-illness study. Lancet Diab Endocrinol. 2017;5(6):423–430. doi:10.1016/S2213-8587(17)30097-9

2. Ma X, Liu Z, Ilyas I, et al. GLP-1 receptor agonists (GLP-1RAs): cardiovascular actions and therapeutic potential. Int J Biol Sci. 2021;17(8):2050–2068. doi:10.7150/ijbs.59965

3. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–1222. doi:10.1016/S0140-6736(20)30925-9

4. Jiang Y, L. W, Zhu X, et al. Advances in management of metabolic dysfunction-associated steatotic liver disease: from mechanisms to therapeutics. Lipids Health Dis. 2024;23(1):95. doi:10.1186/s12944-024-02092-2

5. Younossi ZM, Golabi P, de Avila L, et al. The global epidemiology of NAFLD and NASH in patients with type 2 diabetes: a systematic review and meta-analysis. J Hepatol. 2019;71(4):793–801. doi:10.1016/j.jhep.2019.06.021

6. Doycheva I, Cui J, Nguyen P, et al. Non-invasive screening of diabetics in primary care for NAFLD and advanced fibrosis by MRI and MRE. Aliment Pharmacol Ther. 2016;43(1):83–95. doi:10.1111/apt.13405

7. Zhou YY, Zhou XD, Wu SJ, et al. Synergistic increase in cardiovascular risk in diabetes mellitus with nonalcoholic fatty liver disease: a meta-analysis. Eur J Gastroenterol Hepatol. 2018;30(6):631–636. doi:10.1097/MEG.0000000000001075

8. Targher G, Lonardo A, Byrne CD. Nonalcoholic fatty liver disease and chronic vascular complications of diabetes mellitus. Nat Rev Endocrinol. 2018;14(2):99–114. doi:10.1038/nrendo.2017.173

9. Ratziu V, Bellentani S, Cortez-Pinto H, Day C, Marchesini G. A position statement on NAFLD/NASH based on the EASL 2009 special conference. J Hepatol. 2010;53(2):372–384. doi:10.1016/j.jhep.2010.04.008

10. Marchesini G, Bugianesi E, Forlani G, et al. Nonalcoholic fatty liver, steatohepatitis, and the metabolic syndrome. Hepatology. 2003;37(4):917–923. doi:10.1053/jhep.2003.50161

11. Angulo P. Nonalcoholic fatty liver disease. New Engl J Med. 2002;346(16):1221–1231. doi:10.1056/NEJMra011775

12. Long MT, Zhang X, Xu H, et al. Hepatic fibrosis associates with multiple cardiometabolic disease risk factors: The Framingham heart study. Hepatology. 2021;73(2):548–559. doi:10.1002/hep.31608

13. Harrison SA, Gawrieh S, Roberts K, et al. Prospective evaluation of the prevalence of non-alcoholic fatty liver disease and steatohepatitis in a large middle-aged US cohort. J Hepatol. 2021;75(2):284–291. doi:10.1016/j.jhep.2021.02.034

14. Guan L, Li L, Zou Y, Zhong J, Qiu L. Association between FIB-4, all-cause mortality, cardiovascular mortality, and cardiovascular disease risk among diabetic individuals: NHANES 1999-2008. Front Cardiovasc Med. 2023;10:1172178. doi:10.3389/fcvm.2023.1172178

15. Leoni S, Tovoli F, Napoli L, Serio I, Ferri S, Bolondi L. Current guidelines for the management of non-alcoholic fatty liver disease: a systematic review with comparative analysis. World J Gastroenterol. 2018;24(30):3361–3373. doi:10.3748/wjg.v24.i30.3361

16. Choi SW, Kweon SS, Lee YH, Ryu SY, Nam HS, Shin MH. Association of liver fibrosis biomarkers with overall and CVD mortality in the Korean population: The Dong-gu study. PLoS One. 2022;17(12):e0277729. doi:10.1371/journal.pone.0277729

17. Schonmann Y, Yeshua H, Bentov I, Zelber-Sagi S. Liver fibrosis marker is an independent predictor of cardiovascular morbidity and mortality in the general population. Dig Liver Dis. 2021;53(1):79–85. doi:10.1016/j.dld.2020.10.014

18. Lamacchia O, Sorrentino MR. Diabetes mellitus, arterial stiffness and cardiovascular disease: Clinical implications and the influence of SGLT2i. Curr Vasc Pharmacol 2021;19(2):233–240. doi:10.2174/18756212MTA1aMzIwy

19. Gulsin GS, Graham-Brown MPM, Squire IB, Davies MJ, McCann GP. Benefits of sodium glucose cotransporter 2 inhibitors across the spectrum of cardiovascular diseases. Heart. 2022;108(1):16–21. doi:10.1136/heartjnl-2021-319185

20. Zannad F, Ferreira JP, Pocock SJ, et al. SGLT2 inhibitors in patients with heart failure with reduced ejection fraction: a meta-analysis of the EMPEROR-Reduced and DAPA-HF trials. Lancet. 2020;396(10254):819–829. doi:10.1016/S0140-6736(20)31824-9

21. Mantovani A, Petracca G, Csermely A, Beatrice G, Targher G. Sodium-Glucose Cotransporter-2 inhibitors for treatment of nonalcoholic fatty liver disease: a meta-analysis of randomized controlled trials. Metabolites. 2020;11(1):22. doi:10.3390/metabo11010022

22. Arai T, Atsukawa M, Tsubota A, et al. Effect of sodium-glucose cotransporter 2 inhibitor in patients with non-alcoholic fatty liver disease and type 2 diabetes mellitus: a propensity score-matched analysis of real-world data. Ther Adv Endocr Metab. 2021;12:20420188211000243. doi:10.1177/20420188211000243

23. Dwinata M, Putera DD, Hasan I, Raharjo M. SGLT2 inhibitors for improving hepatic fibrosis and steatosis in non-alcoholic fatty liver disease complicated with type 2 diabetes mellitus: a systematic review. Clin Exp Hepatol. 2020;6(4):339–346. doi:10.5114/ceh.2020.102173

24. Hsu CL, Loomba R. From NAFLD to MASLD: implications of the new nomenclature for preclinical and clinical research[J]. Nat Metab. 2024;600–602. doi:10.1038/s42255-024-00985-1

25. Arai T, Atsukawa M, Tsubota A, et al. Antifibrotic effect and long-term outcome of SGLT2 inhibitors in patients with NAFLD complicated by diabetes mellitus. Hepatol Commun. 2022;6(11):3073–3082. doi:10.1002/hep4.2069

26. 2. Classification and diagnosis of diabetes: Standards of medical care in diabetes-2019. Diabetes Care. 2019;42(Suppl 1):S13–s28. doi:10.2337/dc19-S002

27. Arslan F, Bongartz L, Ten Berg JM, et al. ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: comments from the Dutch ACS working group. Neth Heart J. 2018;26(9):417–421. doi:10.1007/s12471-018-1134-0

28. Ponikowski P, Voors AA, Anker SD, et al. ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: The task force for the diagnosis and treatment of acute and chronic heart failure of the European society of cardiology (ESC). Developed with the special contribution of the heart failure association (HFA) of the ESC. Eur J Heart Fail. 2016;18(8):891–975. doi:10.1002/ejhf.592

29. Luo JY, Du GL, Hao YM, et al. AT1R gene rs389566 polymorphism contributes to MACCEs in hypertension patients. BMC cardiova diso. 2023;23(1):284. doi:10.1186/s12872-023-03223-w

30. Mao L, Zhang X, Hu Y, et al. Nomogram Based on Cytokines for Cardiovascular Diseases in Xinjiang Kazakhs. Mediators Inflamm. 2019;2019:4756295. doi:10.1155/2019/4756295

31. Kjeldsen SE, Farsang C, Sleigh P, Mancia G. WHO/ISH hypertension guidelines–highlights and esh update. J Hypertens. 2001;19(12):2285–2288. doi:10.1097/00004872-200112000-00026

32. Zuo Z, Cui H, Wang M, et al. Diagnostic of FibroTouch and six serological models in assessing the degree of liver fibrosis among patients with chronic hepatic disease: a single-center retrospective study. PLoS One. 2022;17(7):e0270512. doi:10.1371/journal.pone.0270512

33. Becker L, Salameh W, Sferruzza A, et al. Validation of hepascore, compared with simple indices of fibrosis, in patients with chronic hepatitis C virus infection in United States. Clin Gastroenterol Hepatol. 2009;7(6):696–701. doi:10.1016/j.cgh.2009.01.010

34. Kim RG, Deng J, Reaso JN, Grenert JP, Khalili M. Noninvasive Fibrosis Screening in Fatty Liver Disease Among Vulnerable Populations: impact of Diabetes and Obesity on FIB-4 Score Accuracy. Diabetes Care. 2022;45(10):2449–2451. doi:10.2337/dc22-0556

35. Ferguson D, Finck BN. Emerging therapeutic approaches for the treatment of NAFLD and type 2 diabetes mellitus. Nat Rev Endocrinol. 2021;17(8):484–495. doi:10.1038/s41574-021-00507-z

36. Williamson RM, Price JF, Glancy S, et al. Prevalence of and risk factors for hepatic steatosis and nonalcoholic Fatty liver disease in people with type 2 diabetes: the Edinburgh Type 2 Diabetes Study. Diabetes Care. 2011;34(5):1139–1144. doi:10.2337/dc10-2229

37. Williams CD, Stengel J, Asike MI, et al. Prevalence of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis among a largely middle-aged population utilizing ultrasound and liver biopsy: a prospective study. Gastroenterology. 2011;140(1):124–131. doi:10.1053/j.gastro.2010.09.038

38. Jimba S, Nakagami T, Takahashi M, et al. Prevalence of non-alcoholic fatty liver disease and its association with impaired glucose metabolism in Japanese adults. Diabetic Medi. 2005;22(9):1141–1145. doi:10.1111/j.1464-5491.2005.01582.x

39. Targher G, Bertolini L, Padovani R, et al. Prevalence of nonalcoholic fatty liver disease and its association with cardiovascular disease among type 2 diabetic patients. Diabetes Care. 2007;30(5):1212–1218. doi:10.2337/dc06-2247

40. Cotter TG, Rinella M. Nonalcoholic Fatty Liver Disease 2020: the State of the Disease. Gastroenterology. 2020;158(7):1851–1864. doi:10.1053/j.gastro.2020.01.052

41. Targher G, Corey KE, Byrne CD, Roden M. The complex link between NAFLD and type 2 diabetes mellitus – mechanisms and treatments. Nat Rev Gastroenterol Hepatol. 2021;18(9):599–612. doi:10.1038/s41575-021-00448-y

42. Anstee QM, Targher G, Day CP. Progression of NAFLD to diabetes mellitus, cardiovascular disease or cirrhosis. Nat Rev Gastroenterol Hepatol. 2013;10(6):330–344. doi:10.1038/nrgastro.2013.41

43. Loomba R, Wong R, Fraysse J, et al. Nonalcoholic fatty liver disease progression rates to cirrhosis and progression of cirrhosis to decompensation and mortality: a real world analysis of Medicare data. Aliment Pharmacol Ther. 2020;51(11):1149–1159. doi:10.1111/apt.15679

44. Byrne CD. EASL-EASD-EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease. Diabetologia. 2016;59(6):1121–1140. doi:10.1007/s00125-016-3902-y

45. Chalasani N, Younossi Z, Lavine JE, et al. The diagnosis and management of nonalcoholic fatty liver disease: practice guidance from the American Association for the Study of Liver Diseases. Hepatology. 2018;67(1):328–357. doi:10.1002/hep.29367

46. Sinn DH, Kang D, Chang Y, et al. Non-alcoholic fatty liver disease and the incidence of myocardial infarction: a cohort study. J Gastroenterol Hepatol. 2020;35(5):833–839. doi:10.1111/jgh.14856

47. Targher G, Bertolini L, Rodella S, Lippi G, Zoppini G, Chonchol M. Relationship between kidney function and liver histology in subjects with nonalcoholic steatohepatitis. Clin j American Soci Nephr. 2010;5(12):2166–2171. doi:10.2215/CJN.05050610

48. Targher G, Bertolini L, Chonchol M, et al. Non-alcoholic fatty liver disease is independently associated with an increased prevalence of chronic kidney disease and retinopathy in type 1 diabetic patients. Diabetologia. 2010;53(7):1341–1348. doi:10.1007/s00125-010-1720-1

49. Kasper P, Martin A, Lang S, et al. NAFLD and cardiovascular diseases: a clinical review. Clin Rese Cardiol. 2021;110(7):921–937. doi:10.1007/s00392-020-01709-7

50. Zhang T, Luo JY, Liu F, et al. Long noncoding RNA MALAT1 polymorphism predicts MACCEs in patients with myocardial infarction. BMC cardiova diso. 2022;22(1):152. doi:10.1186/s12872-022-02590-0

51. Targher G, Byrne CD, Lonardo A, Zoppini G, Barbui C. Non-alcoholic fatty liver disease and risk of incident cardiovascular disease: a meta-analysis. J Hepatol. 2016;65(3):589–600. doi:10.1016/j.jhep.2016.05.013

52. Lonardo A, Ballestri S, Guaraldi G, et al. Fatty liver is associated with an increased risk of diabetes and cardiovascular disease – Evidence from three different disease models: NAFLD, HCV and HIV. World J Gastroenterol. 2016;22(44):9674–9693. doi:10.3748/wjg.v22.i44.9674

53. Henson JB, Simon TG, Kaplan A, Osganian S, Masia R, Corey KE. Advanced fibrosis is associated with incident cardiovascular disease in patients with non-alcoholic fatty liver disease. Aliment Pharmacol Ther. 2020;51(7):728–736. doi:10.1111/apt.15660

54. Vilar-Gomez E, Chalasani N. Non-invasive assessment of non-alcoholic fatty liver disease: clinical prediction rules and blood-based biomarkers. J Hepatol. 2018;68(2):305–315. doi:10.1016/j.jhep.2017.11.013

55. ElSayed NA, Aleppo G, Aroda VR, et al. 4. Comprehensive Medical Evaluation and Assessment of Comorbidities: standards of Care in Diabetes-2023. Diabetes Care. 2023;46(Suppl 1).

56. Kamada Y, Munekage K, Nakahara T, et al. The FIB-4 Index Predicts the Development of Liver-Related Events, Extrahepatic Cancers, and Coronary Vascular Disease in Patients with NAFLD. Nutrients. 2022;15(1). doi:10.3390/nu15010066

57. Suh B, Park S, Shin DW, et al. High liver fibrosis index FIB-4 is highly predictive of hepatocellular carcinoma in chronic hepatitis B carriers. Hepatology. 2015;61(4):1261–1268. doi:10.1002/hep.27654

58. Qiang S, Nakatsu Y, Seno Y, et al. Treatment with the SGLT2 inhibitor luseogliflozin improves nonalcoholic steatohepatitis in a rodent model with diabetes mellitus. Diabetol Metab Syndr. 2015;7:104. doi:10.1186/s13098-015-0102-8

59. Farkouh ME, Verma S. Prevention of Heart Failure With SGLT-2 Inhibition: insights From CVD-REAL. J Am Coll Cardiol. 2018;71(22):2507–2510. doi:10.1016/j.jacc.2018.02.078

60. Oikonomou D, Georgiopoulos G, Katsi V, et al. Non-alcoholic fatty liver disease and hypertension: coprevalent or correlated?. Eur J Gastroenterol Hepatol. 2018;30(9):979–985. doi:10.1097/MEG.0000000000001191

61. Ala M. SGLT2 Inhibition for Cardiovascular Diseases, Chronic Kidney Disease, and NAFLD. Endocrinology. 2021;162(12). doi:10.1210/endocr/bqab157

62. Puglisi S, Rossini A, Poli R, et al. Effects of SGLT2 Inhibitors and GLP-1 Receptor Agonists on Renin-Angiotensin-Aldosterone System. Front Endocrinol. 2021;12:738848. doi:10.3389/fendo.2021.738848