GRAPHICAL ABSTRACT

INTRODUCTION

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD), is characterized by hepatic steatosis and one or more cardiometabolic risk factors [1]. This condition is particularly common among individuals with metabolic conditions, such as obesity, insulin resistance, hypertension, and dyslipidemia [2]. The global prevalence of MASLD among patients with type 2 diabetes mellitus (T2DM) is approximately 55.5% [3], and this prevalence is expected to increase as obesity and diabetes become more common.

The relationship between MASLD and T2DM is complex and bidirectional. In T2DM, hyperglycemia and insulin resistance contribute to hepatic inflammation, oxidative stress, and fibrogenesis, thereby accelerating the progression of MASLD to non-alcoholic steatohepatitis, advanced fibrosis, or cirrhosis. Conversely, MASLD can worsen glycemic control and increase the risk of T2DM-related complications through its effects on hepatic insulin resistance and systemic inflammation [4,5]. Elucidating the complications arising from the coexistence of these conditions is essential.

Ischemic stroke, a leading cause of disability and mortality worldwide, has increasingly been associated with metabolic disorders [6]. Patients with T2DM have approximately twice the risk of ischemic stroke compared with those without diabetes due to factors such as accelerated atherosclerosis, increased platelet aggregation, and endothelial dysfunction [7–10]. MASLD may share similar mechanisms and also increase the risk of ischemic stroke. A significant association between MASLD severity and ischemic stroke risk has previously been reported [11].

Most studies have focused on the individual effects of MASLD and T2DM on cardiovascular outcomes; however, limited attention has been given to the relationship between MASLD and ischemic stroke in patients with T2DM. Therefore, we aimed to investigate the association between MASLD and ischemic stroke risk in these patients using data from a nationwide cohort. In addition, we examined the impact of alcohol consumption and the degree of steatosis on the risk of ischemic stroke within this population. Furthermore, we investigated the impact of advanced hepatic fibrosis in addition to MASLD on ischemic stroke risk.

METHODS

Data source

This study utilized comprehensive data from the Korean National Health Insurance Service database, which covers the entire South Korean population from January 2002 to December 2018. The database includes two major healthcare programs: National Health Insurance, which serves approximately 97% of the population, and Medical Aid, which provides coverage for the remaining 3%, comprising the lowest-income group. This government-operated database stores extensive patient information, including demographics, health examination records, diagnoses classified according to the International Classification of Diseases, 10th Revision (ICD-10), and prescription data.

This study was approved by the Institutional Review Board of Samsung Medical Center (approval no. SMC 2024-03-104), Seoul, Republic of Korea. Written informed consent was waived by the board.

Study design and population

A nationwide cohort study was conducted involving 2,741,135 individuals with T2DM aged ≥20 years who participated in health screenings between 2009 and 2012. Exclusions were applied to individuals aged <20 years (n=390), those with missing data (n=122,173), and those with a history of liver cancer (n=6,471), liver transplantation (n=567), myocardial infarction (n=111,866), or ischemic stroke (n=253,201). Additional exclusion criteria included patients diagnosed with ischemic stroke or those who died from any cause within the first year of follow-up (n=26,218). After these exclusions, 2,220,249 participants were included in the final analysis (Supplemental Fig. S1).

Measurements and definitions of variables

The measurements obtained during the health examinations included height, weight, and waist circumference (WC). Body mass index (BMI) was calculated by dividing weight in kilograms by the square of height in meters. Chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate of <60 mL/min/1.73 m². T2DM was defined as fasting glucose levels ≥126 mg/dL, use of oral hypoglycemic agents (OHAs), or the presence of medical claims with diagnostic codes E11–14. Hypertension was defined as a systolic blood pressure ≥140 mm Hg, a diastolic blood pressure ≥90 mm Hg, use of antihypertensive medication, or at least one medical claim with diagnostic codes I10–13 and I15. Dyslipidemia was defined as at least one annual medical claim using code E78, together with either a prescription for lipid-lowering agents or a total cholesterol level ≥240 mg/dL. Atrial fibrillation (AF) was diagnosed using ICD-10 codes I480–I484 and I489, recorded in at least one hospitalization record or two or more outpatient clinic records. Income levels were categorized into quartiles (Q) within the study population (Q1: lowest; Q4: highest). Insulin use was defined as receiving one or more prescriptions for insulin annually or three or more prescriptions per year in outpatient settings. Regular exercise was evaluated via questionnaire and defined as meeting one of the following criteria: (1) vigorous activity on ≥3 days/week for at least 20 min/day or (2) moderate-intensity activity on ≥5 days/week for at least 30 min/day. The Charlson comorbidity index (CCI), which includes 12 comorbid conditions, was determined by the presence of at least one medical claim coded using ICD-10 within the previous year.

Definitions of hepatic steatosis and BARD score

Hepatic steatosis was defined as a fatty liver index (FLI) of ≥30. The FLI was calculated using WC, BMI, triglyceride levels, and gamma-glutamyl transferase (GGT), according to the following formula:

FLI=(e0.953×loge(triglyceride)+0.139×BMI+0.718×loge(GGT)+0.053×WC-15.745)/(1+e0.95×loge(triglyceride)+0.139×BMI+0.718×loge(GGT)+0.053×WC-15.745)×100.

The BARD score, a noninvasive tool for assessing the risk of advanced liver fibrosis in patients with NAFLD, was calculated by assigning two points for an aspartate aminotransferase/alanine aminotransferase ratio ≥0.8, one point for BMI ≥28 kg/m², and one point for the presence of diabetes mellitus (DM) [12]. A total score of 2 to 4 indicated advanced hepatic fibrosis.

Classification of study population

MASLD was defined as steatotic liver disease (SLD) (FLI ≥30) with at least one cardiometabolic risk factor. Cardiometabolic risk factors included the following: BMI ≥23 kg/m²; WC ≥90 cm for males or ≥80 cm for females; fasting blood glucose levels ≥100 mg/dL, presence of T2DM, or ongoing T2DM treatment; hypertension (blood pressure ≥130/85 mm Hg or use of antihypertensive medication); dyslipidemia (triglycerides ≥150 mg/dL or lipid-lowering treatment); and low high-density lipoprotein cholesterol levels (≤40 mg/dL for males and ≤50 mg/dL for females).

Informed by the recent Delphi consensus [1], which conceptualizes MASLD, metabolic dysfunction and alcohol-related steatotic liver disease (MetALD), and alcohol-related liver disease (ALD) as a disease spectrum, we further stratified participants with MASLD according to daily alcohol intake to reflect increasing alcohol predominance. This stratification was not intended to imply discrete disease entities, but rather to operationalize graded alcohol predominance within MASLD for epidemiologic risk comparison. Participants were categorized into five groups: (1) no steatosis (FLI <30); (2) MASLD with mild alcohol consumption (<30 g/day for males and <20 g/day for females); (3) MASLD with other combined disease, defined as MASLD with concomitant liver disease, alcohol abuse/misuse, or ALD; (4) MetALD, defined as MASLD with heavy alcohol consumption (30–60 g/day for males and 20–50 g/day for females); and (5) ALD with metabolic dysfunction (MD), defined as MASLD with very heavy alcohol consumption (≥60 g/day for males and ≥50 g/day for females).

Alcohol abuse/misuse or ALD was identified using the following ICD-10 codes: F10, G31.2, G62.1, G72.1, I42.6, K29.2, K70, K86.0, R78.0, T51.0, T51.8, T51.9, X65, Y15, Y90, Y91, Z50.2, and Z71.4. Concomitant liver diseases were also classified using the following ICD-10 codes: viral hepatitis (B15–B19, B00.8, B25.1), drug-induced (toxic) liver disease (K71), hepatic veno-occlusive disease (I82), liver abscess (K75.0, A06.4), hemochromatosis (E83.1), Wilson’s disease (E83.0), alpha-1 antitrypsin deficiency (E88.0), autoimmune hepatitis (K75.4), biliary cholangitis (K74.3–K74.4), other cholangitis (K83, K83.0), and glycogen storage disease (E74).

Outcomes

The primary outcome was the development of ischemic stroke, defined as at least one hospitalization with a diagnosis coded as I63 or I64, accompanied by claims for brain computed tomography (CT) or magnetic resonance imaging (MRI). Follow-up was conducted from the date of the initial health examination until the occurrence of ischemic stroke or the study endpoint on December 31, 2018.

Sensitivity analysis

To evaluate the robustness of the outcome definition, we conducted four sensitivity analyses using progressively stricter criteria for ischemic stroke: (1) ICD-10 code I63 only; (2) I63 only with hospitalization; (3) I63 only with accompanying brain CT or MRI; and (4) I63 only with both hospitalization and brain imaging claims.

Statistical analysis

Continuous variables are presented as mean±standard deviation for normally distributed data and as mean with 95% confidence interval (CI) for non-normally distributed data. Categorical variables are presented as frequencies and percentages. Baseline characteristics were compared using analysis of variance for continuous variables and the chi-square test for categorical variables.

To assess the risk of ischemic stroke, a multivariate Cox proportional hazards regression model was used to provide hazard ratios (HRs) and 95% CIs. Model 1 included unadjusted estimates. Model 2 was adjusted for age and sex. Model 3 included additional adjustments for income, smoking status, regular exercise, CCI scores, BMI, fasting glucose, T2DM duration, insulin use, OHA use, CKD, hypertension, dyslipidemia, and AF.

A subgroup analysis was performed to evaluate interactions between the variables and SLD. The study population was further stratified according to alcohol consumption and FLI categories. Statistical analyses were performed using SAS software version 9.3 (SAS Institute, Cary, NC, USA), and statistical significance was set at P<0.05.

RESULTS

Baseline characteristics

A total of 2,220,249 participants were classified into five groups: 840,091 (37.8%) in the no-steatosis group; 1,067,142 (48.06%) in the MASLD group; 111,076 (5.0%) in the MASLD with other combined disease group; 153,340 (6.91%) in the MetALD group; and 48,600 (2.19%) in the ALD with MD group. The baseline characteristics of the 2,220,249 participants are presented in Table 1.

Among the five groups, the age range of 40 to 64 years accounted for the largest proportion. Participants in the MetALD and ALD with MD groups were predominantly male and exhibited higher rates of current smoking (53.13% and 54.10%, respectively). These groups also demonstrated higher levels of biomarkers such as fasting glucose, triglycerides, and GGT. By contrast, the no-steatosis group consisted mostly of females and had lower BMI, WC, and blood pressure.

Risk of ischemic stroke in different SLD subtypes

During a median follow-up period of 11 years, 135,482 incident cases of ischemic stroke occurred among 2,220,249 participants. Compared with participants without steatosis at baseline, those diagnosed with MASLD, MASLD with other combined disease, MetALD, and ALD with MD had an increased risk of ischemic stroke. The adjusted hazard ratios (aHRs) for these groups were 1.10 (95% CI, 1.08 to 1.11), 1.14 (95% CI, 1.12 to 1.17), 1.13 (95% CI, 1.11 to 1.16), and 1.32 (95% CI, 1.27 to 1.37), respectively, after controlling for confounding variables, including age, sex, income, smoking, regular exercise, CCI score, body mass index, fasting glucose, DM duration, insulin use, OHA use, CKD, hypertension, dyslipidemia, and AF (Model 3, Table 2).

Sensitivity analyses using alternative definitions of ischemic stroke yielded results consistent with the primary analysis, with all SLD categories showing higher risks compared with the no-SLD group (Supplemental Tables S1–S4).

Risk of ischemic stroke according to BARD score in patients with SLD subtypes

An additional analysis evaluated the risk of ischemic stroke according to SLD subtype using the BARD score, a marker of advanced hepatic fibrosis (Table 3). In most SLD groups, patients with a BARD score of ≥2 exhibited a trend toward higher aHRs than those with a score of <2. For example, in the MASLD group, the aHR was 1.03 (95% CI, 1.01 to 1.06) for those with BARD <2, increasing to 1.11 (95% CI, 1.10 to 1.13) among those with BARD ≥2.

Sensitivity analyses assessing the robustness of BARD score components

To evaluate whether the observed associations were influenced by the inclusion of diabetes as a component of the BARD score, we conducted sensitivity analyses using a modified BARD (mBARD) score excluding the diabetes component. Analyses using mBARD thresholds of ≥1 and ≥2 yielded results comparable to those observed with the original BARD score. Higher mBARD categories were generally associated with higher point estimates for ischemic stroke risk across SLD subtypes, with the strongest and most consistent associations observed among individuals with ALD with MD (Supplemental Tables S5, S6). When the mBARD score was modeled as a continuous variable, higher mBARD scores were also associated with an increased risk of ischemic stroke, further supporting the robustness of the associations independent of specific cut-off definitions (Supplemental Table S7).

In BMI-stratified analyses based on the BARD threshold (BMI <28 kg/m² vs. ≥28 kg/m²), associations between SLD categories and ischemic stroke risk were observed in both strata. Effect estimates were generally higher among individuals with BMI ≥28 kg/m², and a statistically significant interaction (P for interaction=0.0079) between BMI category and SLD status was observed (Supplemental Table S8).

Risk of ischemic stroke by alcohol consumption and FLI categories

We further estimated the risk of ischemic stroke according to the degree of hepatic steatosis and alcohol consumption. Patients with alcohol abuse or misuse, ALD, or concomitant liver disease were excluded from the analysis.

In Table 4, the non-drinking group with FLI <30 served as the reference. Within the FLI <30 category, the mild-drinking group showed a decreased aHR of 0.94 (95% CI, 0.91 to 0.96), whereas the heavy- and very heavy-drinking groups exhibited increased aHRs of 1.09 (95% CI, 1.04 to 1.14) and 1.21 (95% CI, 1.10 to 1.33), respectively. In the FLI 30–60 category, the non-drinking, heavy-drinking, and very heavy-drinking groups showed aHRs of 1.09 (95% CI, 1.07 to 1.11), 1.09 (95% CI, 1.05 to 1.13), and 1.29 (95% CI, 1.21 to 1.38), respectively. Among patients with FLI ≥60, the aHRs were 1.19 (95% CI, 1.17 to 1.21) for the non-drinking group, 1.09 (95% CI, 1.06 to 1.11) for the mild-drinking group, 1.13 (95% CI, 1.09 to 1.17) for the heavy-drinking group, and 1.27 (95% CI, 1.21 to 1.34) for the very heavy-drinking group (Fig. 1).

Overall, the mild-drinking group exhibited a relatively lower point estimate for ischemic stroke risk, whereas higher risks were observed in the heavy- and very heavy-drinking groups. To further evaluate this observed trend, we analyzed the data by setting the non-drinking group as the reference within each FLI category (Table 5, Fig. 2). When non-drinking was used as the reference within each FLI category, mild alcohol consumption was associated with lower HRs for ischemic stroke. By contrast, heavy drinking did not show a consistent pattern, whereas very heavy drinking tended to be associated with higher risk across FLI categories.

In sex-stratified analyses (Supplemental Tables S9, S10), the overall patterns of association between FLI categories, alcohol consumption, and ischemic stroke risk were broadly similar in men and women, although the magnitude and statistical significance of the effect estimates varied by sex. In contrast to mild alcohol consumption, heavy and very heavy drinking were generally associated with higher risk estimates, particularly among individuals with higher FLI categories. Among individuals with high FLI (≥60), heavy alcohol consumption was significantly associated with an increased risk of ischemic stroke in both men and women. In contrast, associations in some female subgroups, especially at lower FLI levels, did not reach statistical significance, which may partly reflect the lower prevalence of heavy drinking and limited statistical power in these groups.

Subgroup analysis

Subgroup analyses of ischemic stroke risk according to several variables are shown in Supplemental Table S11. The association between SLD subtypes and stroke risk exhibited a similar trend across subgroups. However, interactions were observed with age, sex, BMI, DM duration, fasting glucose levels, DM medication, exercise, and the presence of hypertension and dyslipidemia (P for interaction <0.05).

DISCUSSION

This study demonstrated that SLD, including MASLD, Met-ALD, and ALD with MD, was associated with an increased risk of ischemic stroke in patients with T2DM in a large nationwide cohort. Notably, patients with T2DM and MASLD who had a BARD score of ≥2, indicating advanced liver fibrosis, exhibited a significantly higher risk of ischemic stroke. These findings highlight the importance of assessing hepatic fibrosis severity when evaluating stroke risk in this population. In addition, higher FLI values were positively associated with an increased risk of ischemic stroke.

Our findings support the hypothesis that MASLD is not only a marker of MD but also a contributor to systemic vascular pathology [13]. Several studies have demonstrated a link between NAFLD and an increased risk of subclinical atherosclerosis and cardiovascular disease (CVD) across different populations. A meta-analysis of 16 observational studies involving 34,043 participants demonstrated a significant association between NAFLD and CVD outcomes (odds ratio, 1.64; 95% CI, 1.26 to 2.13) [14]. In particular, several studies have investigated the risk of CVD in individuals with NAFLD and T2DM, although most were limited by relatively small sample sizes (fewer than 1,000 participants) [15–17]. Recent large-scale cohort studies have examined the associations between NAFLD, cardiovascular risk, and mortality in patients with T2DM [18,19]. These studies assessed the risk of composite cardiovascular events, including myocardial infarction and stroke. However, no previous study has specifically examined the risk of ischemic stroke in patients with T2DM and newly defined MASLD. Furthermore, our study demonstrated that advanced hepatic fibrosis (defined by a BARD score of ≥2) was associated with a higher risk of ischemic stroke in individuals with MASLD in the presence of T2DM. Liver fibrosis is the key histologic feature linked to adverse long-term outcomes in NAFLD, independent of steatohepatitis or activity score, and in patients with T2DM, advanced fibrosis (estimated by noninvasive indices) is independently associated with increased CVD risk, including myocardial infarction and stroke [20]. Given that T2DM is associated with increased susceptibility to advanced hepatic fibrosis, our findings suggest that the fibrotic phenotype of MASLD, characterized by inflammation and hepatic injury, may further elevate stroke risk. The underlying mechanisms linking MASLD and/or advanced liver fibrosis to ischemic stroke in patients with T2DM likely involve systemic insulin resistance, oxidative stress, dysregulated lipid metabolism, endothelial dysfunction, and chronic low-grade inflammation [3].

We also analyzed the risk of ischemic stroke according to FLI and alcohol consumption. Across FLI categories, the risk of ischemic stroke increased among individuals with heavy and very heavy drinking, whereas those with mild alcohol consumption showed lower observed risk estimates compared with nondrinkers. To further explore this pattern, we conducted additional analyses using non-drinkers as the reference group within each FLI category, in which mild drinkers continued to demonstrate lower observed risk estimates. Similar patterns have been reported in previous observational studies examining alcohol consumption and stroke risk [21–23]. However, because alcohol consumption was assessed using self-reported health screening data at a single time point, we were unable to distinguish lifelong abstainers from former drinkers. As a result, the lower observed risk among individuals with mild alcohol consumption may reflect residual confounding, exposure misclassification, or sick-quitter bias rather than a true protective effect. Consistent with this interpretation, sex-stratified analyses showed that mild alcohol consumption was associated with lower risk estimates compared with non-drinking across FLI categories; however, this inverse association should be interpreted cautiously and does not necessarily indicate a causal protective effect. In contrast, higher FLI values were associated with a greater incidence of ischemic stroke, and the risk increased substantially in the very heavy-drinking group across all FLI categories. Taken together, these findings indicate that very heavy drinking was generally associated with a higher risk of ischemic stroke across FLI categories, whereas the lower observed risk among individuals with mild alcohol consumption should be interpreted cautiously in light of these methodological limitations.

Subgroup analysis revealed that female patients with SLD were more susceptible to ischemic stroke than male patients. Notably, we observed a higher ischemic stroke risk in patients with new-onset DM than in those with preexisting DM. Additionally, patients not taking antidiabetic medication exhibited a greater stroke risk than those receiving OHA or insulin. These findings may reflect differences in metabolic control, including hyperglycemia and insulin resistance, as well as potential delays in achieving optimal glycemic management after diabetes diagnosis. However, given the observational nature of the study, these subgroup findings should be interpreted cautiously. Further studies are warranted to clarify the mechanisms underlying these associations and to determine whether early metabolic interventions influence stroke risk.

This study had several strengths. First, a nationwide, long-term cohort design was used to investigate the association between ischemic stroke risk and MASLD in patients with T2DM. In addition to MASLD status, the presence of advanced hepatic fibrosis, the degree of hepatic steatosis, and alcohol consumption were assessed in the analyses. Second, the use of Cox proportional hazards models enabled precise adjustment for multiple confounders, including demographic variables, lifestyle factors, and comorbidities, thus strengthening the reliability of the observed associations between MASLD categories and ischemic stroke risk.

However, some limitations should be acknowledged. First, we defined hepatic steatosis and advanced liver fibrosis using the FLI and BARD score, respectively, as validated noninvasive screening tools rather than radiologic imaging or histologic findings. While this may limit the ability to directly assess liver pathology, both indices have been well validated, are widely used, and show good predictive value, including in Korean populations [24,25]. Because the FLI and BARD score incorporate metabolic components such as BMI, some degree of construct overlap with ischemic stroke risk factors is unavoidable. Accordingly, the observed associations should be interpreted with caution, and this limitation cannot be fully eliminated despite additional sensitivity analyses. Although fibrosis indices such as fibrosis-4 (FIB-4) are emphasized in recent clinical guidelines, FIB-4 could not be assessed because platelet count data were not available in the Korean National Health Insurance Service database. Second, alcohol consumption was assessed at a single time point and relied on self-reported data, which may not accurately reflect long-term drinking patterns. Specifically, the amount of alcohol consumption was measured only at the time of the medical examination, limiting our ability to distinguish among lifelong abstainers, former drinkers, and individuals with fluctuating or episodic drinking behaviors. As a result, alcohol exposure may have been subject to measurement error, potentially leading to misclassification of alcohol intake categories. Given that MD and alcohol-related risk exist along a continuum, such misclassification may have contributed to partial overlap among MASLD, MetALD, and ALD with MD. Consequently, the stratification of alcohol-related risk and its association with ischemic stroke outcomes may have been attenuated or biased. These limitations are inherent to large-scale observational studies and should be considered when interpreting our findings.

Third, ischemic stroke was identified using administrative claims data rather than adjudicated clinical diagnoses. Although hospitalization and brain imaging claims were required to enhance diagnostic specificity, some outcome misclassification may remain. To address this, we conducted multiple sensitivity analyses using increasingly stringent definitions, including restriction to ICD-10 code I63, which yielded results consistent with the primary analysis. Any remaining misclassification is unlikely to differ across SLD categories and would therefore tend to bias estimates toward the null, suggesting that our observed HRs may be conservative. Fourth, despite extensive adjustments, residual confounding cannot be ruled out. Unmeasured factors, such as detailed dietary habits, genetic predispositions, medication use, longitudinal control of blood pressure and lipid levels, and broader socioeconomic factors, may influence both the progression of metabolic-associated SLD and the risk of ischemic stroke.

In this large nationwide cohort of patients with T2DM, advanced liver fibrosis assessed by the BARD score, as well as SLD categories including MASLD, MetALD, and ALD with MD, were associated with an increased risk of ischemic stroke. Additionally, very heavy drinking was generally associated with a higher risk of ischemic stroke across FLI categories. Overall, these findings suggest that SLD phenotypes and noninvasive fibrosis indices may help identify individuals with T2DM at higher risk of ischemic stroke. However, given the observational nature of this study and the potential for unmeasured confounding, the clinical utility of incorporating SLD or fibrosis indices into stroke risk stratification remains uncertain. Further prospective studies and randomized controlled trials are needed to determine whether interventions targeting metabolic liver disease can improve stroke-related outcomes.

Supplementary Material

Article information

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Conception or design: G.K., K.H., J.H.K. Acquisition, analysis, or interpretation of data: M.J., K.L., K.H. Drafting the work or revising: M.J., G.K., J.H.K. Final approval of the manuscript: M.J., G.K., K.L., R.O., S.H.C., J.Y.K., Y.B.L., S.M.J., K.Y.H., K.H., J.H.K.

Fig. 1

Adjusted hazard ratios for ischemic stroke development according to cut-off values of the fatty liver index (FLI) and alcohol consumption. Data are presented as adjusted hazard ratios (95% confidence intervals). The reference group (Ref) is non-drinkers with FLI <30.

Fig. 2

Adjusted hazard ratios for ischemic stroke development according to cut-off values of the fatty liver index (FLI) and alcohol consumption. Data are presented as adjusted hazard ratios (95% confidence intervals), with the non-drinking group within each FLI category serving as the reference group.

References

• 1. Rinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology 2023;78:1966–86.PubMedPMC
• 2. Marchesini G, Bugianesi E, Forlani G, Cerrelli F, Lenzi M, Manini R, et al. Nonalcoholic fatty liver, steatohepatitis, and the metabolic syndrome. Hepatology 2003;37:917–23.ArticlePubMed
• 3. Younossi ZM, Golabi P, de Avila L, Paik JM, Srishord M, Fukui N, et al. The global epidemiology of NAFLD and NASH in patients with type 2 diabetes: a systematic review and meta-analysis. J Hepatol 2019;71:793–801.ArticlePubMed
• 4. 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;18:599–612.ArticlePubMedPDF
• 5. Tilg H, Moschen AR, Roden M. NAFLD and diabetes mellitus. Nat Rev Gastroenterol Hepatol 2017;14:32–42.ArticlePubMedPDF
• 6. Martin SS, Aday AW, Almarzooq ZI, Anderson CA, Arora P, Avery CL, et al. 2024;Heart disease and stroke statistics: a report of US and global data from the American Heart Association. Circulation 2024;149:e347–913.PubMedPMC
• 7. Maida CD, Daidone M, Pacinella G, Norrito RL, Pinto A, Tuttolomondo A. Diabetes and ischemic stroke: an old and new relationship an overview of the close interaction between these diseases. Int J Mol Sci 2022;23:2397.ArticlePubMedPMC
• 8. Kannel WB, McGee DL. Diabetes and cardiovascular disease: the Framingham study. JAMA 1979;241:2035–8.ArticlePubMed
• 9. Kissela BM, Khoury J, Kleindorfer D, Woo D, Schneider A, Alwell K, et al. Epidemiology of ischemic stroke in patients with diabetes: the greater Cincinnati/Northern Kentucky Stroke Study. Diabetes Care 2005;28:355–9.PubMed
• 10. Almdal T, Scharling H, Jensen JS, Vestergaard H. The independent effect of type 2 diabetes mellitus on ischemic heart disease, stroke, and death: a population-based study of 13,000 men and women with 20 years of follow-up. Arch Intern Med 2004;164:1422–6.ArticlePubMed
• 11. Xu J, Dai L, Zhang Y, Wang A, Li H, Wang Y, et al. Severity of nonalcoholic fatty liver disease and risk of future ischemic stroke events. Stroke 2021;52:103–10.ArticlePubMed
• 12. Cichoz-Lach H, Celinski K, Prozorow-Krol B, Swatek J, Slomka M, Lach T. The BARD score and the NAFLD fibrosis score in the assessment of advanced liver fibrosis in nonalcoholic fatty liver disease. Med Sci Monit; 2012. 18:p. CR735–40.ArticlePubMedPMC
• 13. Balta S. Atherosclerosis and non-alcoholic fatty liver disease. Angiology 2022;73:701–11.ArticlePubMedPDF
• 14. 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;65:589–600.ArticlePubMed
• 15. Targher G, Bertolini L, Padovani R, Poli F, Scala L, Tessari R, et al. Increased prevalence of cardiovascular disease in type 2 diabetic patients with non-alcoholic fatty liver disease. Diabet Med 2006;23:403–9.ArticlePubMed
• 16. Targher G, Bertolini L, Rodella S, Tessari R, Zenari L, Lippi G, et al. Nonalcoholic fatty liver disease is independently associated with an increased incidence of cardiovascular events in type 2 diabetic patients. Diabetes Care 2007;30:2119–21.ArticlePubMedPDF
• 17. Targher G, Bertolini L, Poli F, Rodella S, Scala L, Tessari R, et al. Nonalcoholic fatty liver disease and risk of future cardiovascular events among type 2 diabetic patients. Diabetes 2005;54:3541–6.ArticlePubMedPDF
• 18. Wild SH, Walker JJ, Morling JR, McAllister DA, Colhoun HM, Farran B, et al. Cardiovascular disease, cancer, and mortality among people with type 2 diabetes and alcoholic or nonalcoholic fatty liver disease hospital admission. Diabetes Care 2018;41:341–7.ArticlePubMedPDF
• 19. Kim KS, Hong S, Han K, Park CY. Association of non-alcoholic fatty liver disease with cardiovascular disease and all cause death in patients with type 2 diabetes mellitus: nationwide population based study. BMJ 2024 384:e076388.Article
• 20. Angulo P, Kleiner DE, Dam-Larsen S, Adams LA, Bjornsson ES, Charatcharoenwitthaya P, et al. Liver fibrosis, but no other histologic features, is associated with long-term outcomes of patients with nonalcoholic fatty liver disease. Gastroenterology 2015;149:389–97.ArticlePubMedPMC
• 21. Sacco RL, Elkind M, Boden-Albala B, Lin IF, Kargman DE, Hauser WA, et al. The protective effect of moderate alcohol consumption on ischemic stroke. JAMA 1999;281:53–60.ArticlePubMed
• 22. Reynolds K, Lewis B, Nolen JD, Kinney GL, Sathya B, He J. Alcohol consumption and risk of stroke: a meta-analysis. JAMA 2003;289:579–88.ArticlePubMed
• 23. Patra J, Taylor B, Irving H, Roerecke M, Baliunas D, Mohapatra S, et al. Alcohol consumption and the risk of morbidity and mortality for different stroke types: a systematic review and meta-analysis. BMC Public Health 2010;10:258.ArticlePubMedPMCPDF
• 24. Han AL. Validation of fatty liver index as a marker for metabolic dysfunction-associated fatty liver disease. Diabetol Metab Syndr 2022;14:44.ArticlePubMedPMCPDF
• 25. Cho EJ, Jung GC, Kwak MS, Yang JI, Yim JY, Yu SJ, et al. Fatty liver index for predicting nonalcoholic fatty liver disease in an asymptomatic Korean population. Diagnostics (Basel) 2021;11:2233.ArticlePubMedPMC

Figure & Data

References

Citations

Citations to this article as recorded by