SUMMARY
Background:
There are limited data on the prevalence and treatment for at-risk metabolic dysfunction-associated steatohepatitis (MASH) among patients with type 2 diabetes (T2DM) in the United States.
Aim:
To estimate the prevalence of at-risk MASH in a prospectively recruited cohort of adults with T2DM using new nomenclature endorsed by multiple societies
Methods:
This prospective study enrolled adults age ≥ 50 with T2DM from primary care and endocrinology clinics in southern California from 2016 to 2023. Metabolic dysfunction-associated steatotic liver disease (MASLD) was defined by an MRI-PDFF ≥ 5% and at least one metabolic risk factor without any other chronic liver disease or secondary cause for hepatic steatosis.
Results:
We included 530 adult patients with T2DM. The mean (±SD) age and body mass index (BMI) were 64.4 (±8.1) years and 31.5 (±6.1) kg/m2, respectively. Among patients with T2DM, the prevalence of MASLD, at-risk MASH and cirrhosis was 69.6%, 13.6%, and 6.8%, respectively. Among patients with co-existing T2DM and obesity, the prevalence of MASLD, at-risk MASH and cirrhosis was 77.8%, 15.9%, and 9.0% and was higher than in participants without obesity (p-value < 0.0001, 0.0543, 0.0128, respectively).
Conclusion:
Among adults aged ≥ 50 years with T2DM, the prevalence of MASLD, at-risk MASH, and cirrhosis is high, posing a significant risk for liver-related morbidity and mortality. Approximately 14% of patients with T2DM may be candidates for pharmacologic therapies specific for MASH-related fibrosis.
Keywords: metabolic dysfunction-associated steatohepatitis, type 2 diabetes mellitus, prevalence, metabolic dysfunction-associated steatotic liver disease
Graphical Abstract
Among adults aged ≥ 50 years with T2DM, the prevalence of MASLD, at-risk MASH, and cirrhosis is high, posing a significant risk for liver-related morbidity and mortality. Approximately 14% of patients with T2DM may be candidates for pharmacologic therapies specific for MASH-related fibrosis.
Introduction
The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD)1 in Type 2 Diabetes (T2DM) ranges from 50–70%2–5. The spectrum of MASLD includes isolated steatosis which may progress to metabolic dysfunction-associated steatohepatitis (MASH),1 fibrosis, cirrhosis, and hepatocellular carcinoma. Patients with significant fibrosis (fibrosis stage 2 or higher, (≥F2))6 or advanced fibrosis (fibrosis stage 3 or higher, ≥F3)7,8 face increased risks of morbidity and mortality. Consequently, clinical trials, particularly phase 3 registry trials, have prioritized MASH with ≥F2, or at-risk MASH6.
Patients with at-risk MASH are eligible to pharmacological treatment according to the FDA and EMA9. There is a robust pipeline of drugs to resolve MASH, reverse fibrosis, or both. Some of the therapies have beneficial impacts that go beyond the liver, including an improvement in glycemic control, lipid profile, and weight loss9. Three non-invasive stiffness-based models, MEFIB (magnetic resonance elastography [MRE] plus FIB-4), MAST (magnetic resonance imaging [MRI]-aspartate aminotransferase [AST]), and FAST (FibroScan-AST) are used to diagnose at-risk MASH10,11. Magnetic resonance elastography (MRE) is the most accurate, quantitative biomarker of liver fibrosis with a higher specificity and positive predictive value for liver fibrosis compared to vibration controlled transient elastography (VCTE)12,13. Similarly, magnetic resonance imaging proton density fat fraction (MRI-PDFF) has higher diagnostic accuracy compared to controlled at attenuation parameter, an ultrasound-based measure used to measure hepatic fat12,13.
Despite the high risks secondary to at-risk MASH in type 2 diabetics, there is a lack of data investigating the utility of non-invasive testing to screen for and manage this disease. In our study, we aim to better understand the efficacy of non-invasive testing to quantify the prevalence of at-risk MASH among a prospectively recruited cohort of adults with T2DM. Secondary outcomes include quantifying the prevalence of MASLD and cirrhosis among this population.
Materials and Methods:
Study Design
This prospective study assessed the prevalence of at-risk MASH among patients who were diagnosed and being treated for T2DM. We enrolled participants from primary care and endocrinology clinics in the San Diego community. Participants were also recruited through educational brochures, ads in local newspapers, local fairs, and social media. The study ultimately included 530 patients who underwent a research visit that consisted of a thorough clinical evaluation, physical exam, laboratory tests, and imaging modalities including vibration controlled transient elastography (VCTE), controlled attenuation parameter (CAP), magnetic resonance elastography (MRE), and Magnetic Resonance Imaging-Proton Density Fat Fraction (MRI-PDFF)14–18.
We collected data between 2016 and 2023 which we further analyzed at the UCSD MASLD Research Center. All patients provided written consent prior to enrolling in the study; the study was approved by the UCSD Institutional Review Board.
Inclusion and Exclusion Criteria
Our inclusion criteria included participants who were 50–80 years old and had an established diagnosis of T2DM according to the American Diabetes Association based on meeting one of the following criteria: diabetes symptoms and plasma glucose ≥ 200 mg/dl, fasting plasma glucose ≥ 126 mg/dl, plasma glucose ≥200 mg/dl during a 75-g oral glucose tolerance test on two separate tests, or HbA1c ≥ 6.5%. We excluded participants if they had significant alcohol intake (>14 drinks/weeks for men or >7 drinks/week for women) within the previous 2-year period or laboratory evidence of liver disease aside from MASLD.
Clinical Assessment and Laboratory Tests
All patients underwent a detailed clinical evaluation where we collected their vital signs, height, weight, weight circumference, blood pressure, and cholesterol levels. We also elicited their alcohol consumption and respectively proceeded with the Alcohol Use Disorders Identifications Test (AUDIT) and the Skinner Questionnaire to see if alcohol consumption affected their risk of developing liver disease. Fasting laboratory testing was performed and included levels for glucose, albumin, HbA1c, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin, alkaline phosphatase, lipids, platelets, insulin, and international normalized ratio (INR). We used the results of the laboratory testing to estimate the FIB-419 and at-risk MASH fibrosis score.
Magnetic Resonance Imaging
Our participants underwent a non-contrast MR (magnetic resonance) exam with liver fat quantification; we used MRI-PDFF to measure hepatic fat and MRE to measure liver stiffness. Liver stiffness data was acquired using 2D mRE at 60 Hz. Imaging was performed at the UCSD MR3T Research Laboratory using a 3T research scanner (GE Signa EXCITE HDxt; GE Healthcare, Waukesha, WI).
Vibration Controlled Transient Elastography:
Using FibroScan® (Echosens)20, we obtained CAP for the detection of liver fat and VCTE for the quantification of liver stiffness. All exams were performed by an experienced technician after patients had fasted for a minimum of 4 hours. Patients underwent breath holding for a minimum of 10 repeated valid measurements that were assessed automatically by the FibroScan® system. We scanned participants using the M probe (3.5 mHz); if indicated, we re-scanned them using the XL probe (2.5 MHz).
Outcome Measures
Primary outcome
At-risk MASH was defined by an either positive MEFIB score (MRE > 3.3 kPa + Fib4 > 1.6), FAST score ≥ 0.67, or MAST score ≥ 0.2426.
Secondary outcomes
MASLD was defined as MRI-PDFF ≥ 5% or CAP ≥ 288 dB/m among individuals who consumed little to no alcohol and did not have any secondary causes of liver disease per the American Association for the Study of Liver Diseases (AASLD) nomenclature1. We defined cirrhosis as MRE ≥ 4.67 kPa, or if not available VCTE ≥ 15 kPa21,22.
Statistical Analysis
We performed t-tests for patient characteristics presented as the mean (SD); Wilcoxon rank sum tests were used for those presented as median (IQR). Chi-square or Fisher’s exact test were performed for all categorical variables. We proceeded with unadjusted logistic regression to assess for the association between MASLD, at-risk MASH, and cirrhosis with age, sex, ethnicity, obesity, duration of T2DM, and the presence of metabolic syndrome (defined by the joint Societies 2009 criteria). Variables that were found to be significant (p<0.05) were separately evaluated for primary and secondary outcomes.
Results
Characteristics of Study Population
594 patients were screened for our study. Of those, 9 did not meet the age for inclusion and 3 did not meet criteria for a diagnosis of diabetes; 18 others were excluded per our exclusion criteria (Figure 1). Of the 564 patients with T2DM remaining, 24 more were missing some component necessary for classification and 10 more were excluded as they met criteria for MetALD or ALD. (Figure 1). Ultimately, 530 patients were enrolled in the study.
Figure 1:
Derivation of study cohort. T2DM, Type 2 Diabetes.
Participants had mean age of 64.4 (±8.1) and were majority female. The mean (IQR) HbA1C and HOMO-IR were 6.8% (1.6) and 4.9% (5.4) respectively. 452 patients underwent MRE and had liver fat measured on MRI-PDFF. The mean liver stiffness on MRE was 2.7 (± 1.1) while the mean (±SD) liver fat on MRI-PDFF was 9.9% (±8.0). 525 patients underwent VCTE and 524 of those patients also had CAP. The mean liver stiffness on VCTE was 7.4 kPa (± 6.0) while the mean (±SD) CAP was 308.7 dB/m ((±57).
Prevalence of MASLD in T2DM
MASLD was defined as MRI-PDFF ≥ 5% or if not available, CAP ≥ 288 dB/m1. The prevalence of MASLD in patients with T2DM was 69.6% (n = 369). MASLD was associated with higher HbA1c levels, AST, ALT, Triglycerides, LDL, and increased rates of metabolic syndrome (Table 2). The mean CAP, VCTE, MRI-PDFF, and MRE scores were significantly higher in the patients with MASLD over patients without MASLD. (Table 2).
Table 2.
Characteristics by MASLD Status
| Total N=530 | No MASLD N=161 | MASLD N=369 | P value | |
|---|---|---|---|---|
| Demographic and clinical | ||||
| Age in years, mean (SD) | 64.4 (8.1) | 67.3 (7.9) | 63.1 (7.9) | <0.0001 |
| Female, n (%) | 336 (63.4%) | 89 (55.3%) | 247 (66.9%) | 0.0104 |
| BMI (kg/m2), mean (SD) | 31.5 (6.1) | 29.3 (5.5) | 32.4 (6.1) | <0.0001 |
| Obesity (BMI ≥ 30 kg/m2), n (%) | 302 (57.1%) | 67 (41.6%) | 235 (63.9%) | <0.0001 |
| Race, n (%) | 0.0615 | |||
| White | 189 (36.3%) | 51 (32.5%) | 138 (38.0%) | |
| Hispanic | 215 (41.4%) | 77 (49.0%) | 138 (38.0%) | |
| Asian | 80 (15.4%) | 17 (10.8%) | 63 (17.4%) | |
| Other | 36 (6.9%) | 7 (7.6%) | 24 (6.6%) | |
| Duration of T2DM (years), median (IQR) | 12 (13) | 15 (15) | 10 (12) | 0.0005 |
| Hypertension, n (%) | 333 (62.8%) | 100 (62.1%) | 233 (63.1%) | 0.8212 |
| Hyperlipidemia, n (%) | 293 (55.3%) | 95 (59.0%) | 198 (53.7%) | 0.2548 |
| Metabolic syndrome, n (%) | 443 (92.5%) | 123 (86.6%) | 320 (95.0%) | 0.0016 |
| Biochemical profile, median (IQR) | ||||
| HbA1c (%) | 6.8 (1.6) | 6.6 (1.5) | 6.9 (1.5) | 0.0055 |
| HOMA-IR | 4.9 (5.4) | 3.4 (3.3) | 5.9 (6.4) | <0.0001 |
| AST (U/L) | 25 (15) | 20 (8) | 27 (17) | <0.0001 |
| ALT (U/L) | 25 (22) | 18 (8) | 31 (24) | <0.0001 |
| Alkaline phosphatase (U/L) | 80 (34) | 75 (33) | 82 (35.5) | 0.0062 |
| Total bilirubin (mg/dl) | 0.5 (0.2) | 0.5 (0.3) | 0.5 (0.2) | 0.6855 |
| Albumin (g/dl) | 4.4 (0.3) | 4.4 (0.4) | 4.5 (0.3) | 0.9569 |
| Triglycerides (mg/dl) | 143 (93) | 120 (64) | 159.5 (93) | <0.0001 |
| HDL (mg/dl) | 45 (16) | 50 (21) | 44 (15) | <0.0001 |
| LDL (mg/dl) | 86 (45) | 78 (36) | 88 (49) | 0.0145 |
| Platelet count (109/L) | 243 (84) | 238 (78) | 247 (86) | 0.1999 |
| INR | 1 (0.1) | 1 (0.1) | 1 (0.1) | 0.7556 |
| Imaging, mean (SD) | ||||
| CAP (dB/m) | 308.7 (56.4) | 266.8 (51.1) | 326.7 (48.5) | <0.0001 |
| VCTE (kPa) | 7.4 (6.0) | 5.1 (1.7) | 8.4 (6.8) | <0.0001 |
| MRI-PDFF (%) | 9.9 (8.0) | 2.6 (1.4) | 13.2 (7.6) | <0.0001 |
| MRE (kPa) | 2.7 (1.2) | 2.2 (0.4) | 2.9 (1.3) | <0.0001 |
MASLD defined as MRI-PDFF ≥ 5% or, if MRI was not available, as CAP ≥288 db/min. t test performed on continuous variables presented as mean (SD), Wilcoxon rank sum test on all other continuous variables. Level of significance p< 0.05, n = 525 with VCTE data, n = 524 with CAP, n = 452 with MRE, and n = 452 with MRI-PDFF data. ALT, alanine aminotransferase; AST, aspartate aminotransferase; CAP, controlled-attenuation parameter; FIB-4, fibrosis-4 index; HbA1c, hemoglobin A1c; HOMA-IR, homeostatic model assessment of insulin resistance; INR, international normalized ratio; MRE, magnetic resonance elastography; MRI-PDFF, MRI-proton density fat fraction; MASLD, Metabolic dysfunction-associated steatotic liver disease; T2DM, type 2 diabetes mellitus.
Prevalence of at risk-MASH in T2DM
At risk-MASH was determined based on meeting criteria for any of the following definitions based on AASLD Practice guidelines: 1) A FAST score ≥ 0.67, 2) A MAST score of ≥ 0.242, or 3) MEFIB (the combination of MRE and Fib4) ≥ 1 (or FIB-4 ≥ 1.6 kPa plus MRE ≥ 3.3 kPa)6. The prevalence of at-risk MASH in patients with T2DM was 13.6% based on meeting any of the above criteria (n = 72, Table 1).
Table 1.
Characteristics by “at-Risk” MASH Status
| Total N=530 | No “at-risk” MASH N=458 | “at-risk” MASH N=72 | P value | |
|---|---|---|---|---|
| Demographic and clinical | ||||
| Age in years, mean (SD) | 64.4 (8.1) | 64.4 (8.3) | 64.3 (7.2) | <0.0001 |
| Female, n (%) | 336 (63.4%) | 287 (62.7%) | 49 (68.1%) | 0.3773 |
| BMI (kg/m2), mean (SD) | 31.5 (6.1) | 31.3 (6.1) | 32.6 (5.7) | <0.0001 |
| Obesity (BMI ≥ 30 kg/m2), n (%) | 302 (57.1%) | 254 (55.5%) | 48 (67.6%) | 0.0543 |
| Race, n (%) | 0.5162 | |||
| White | 189 (36.3%) | 160 (35.7%) | 29 (40.3%) | |
| Hispanic | 215 (41.4%) | 191 (42.6%) | 24 (33.3%) | |
| Asian | 80 (15.4%) | 67 (15.0%) | 13 (18.1%) | |
| Other | 36 (6.9%) | 30 (6.7%) | 6 (8.3%) | |
| Duration of T2DM (years), median (IQR) | 12 (13) | 12 (12) | 10 (12) | 0.3396 |
| Hypertension, n (%) | 333 (62.8%) | 285 (62.2%) | 48 (66.7%) | 0.4678 |
| Hyperlipidemia, n (%) | 293 (55.3%) | 253 (55.2%) | 40 (55.6%) | 0.9601 |
| Metabolic syndrome, n (%) | 443 (92.5%) | 382 (92.3%) | 61 (93.9%) | 0.6542 |
| Advanced Fibrosis, n(%) | 76 (14.4%) | 26 (5.7%) | 50 (69.4%) | <0.0001 |
| Cirrhosis, n(%) | 36 (6.8%) | 5 (1.1%) | 31 (43.1%) | <0.0001 |
| Biochemical profile, median (IQR) | ||||
| HbA1c (%) | 6.8 (1.6) | 6.8 (1.5) | 7 (1.7) | 0.1512 |
| HOMA-IR | 4.9 (5.4) | 4.6 (4.9) | 7.5 (9.2) | <0.0001 |
| AST (U/L) | 25 (15) | 23 (11) | 53.5 (35.5) | <0.0001 |
| ALT (U/L) | 25 (22) | 23 (18) | 54.5 (42) | <0.0001 |
| Alkaline phosphatase (U/L) | 80 (34) | 79 (33) | 89 (45) | 0.0012 |
| Total bilirubin (mg/dl) | 0.5 (0.2) | 0.4 (0.2) | 0.5 (0.4) | 0.0028 |
| Albumin (g/dl) | 4.4 (0.3) | 4.5 (0.3) | 4.4 (0.4) | 0.1208 |
| Triglycerides (mg/dl) | 143 (93) | 143 (90) | 139.5 (97) | 0.8187 |
| HDL (mg/dl) | 45 (16) | 46 (16) | 44 (19) | 0.2806 |
| LDL (mg/dl) | 86 (45) | 85 (46) | 89 (41) | 0.9024 |
| Platelet count (109/L) | 243 (84) | 251 (79) | 204 (70.5) | <0.0001 |
| INR | 1 (0.1) | 1 (0.1) | 1.1 (0.1) | <0.0001 |
| FIB-4 (SD) | 1.51 (0.85) | 1.33 (0.55) | 2.63 (1.39) | <0.0001 |
| Imaging, mean (SD) | ||||
| CAP (dB/m) | 308.7 (56.4) | 306.8 (55.2) | 321.1 (63.2) | 0.0482 |
| VCTE (kPa) | 7.4 (6.0) | 6.1 (3.3) | 15.6 (10.8) | <0.0001 |
| MRI-PDFF (%) | 9.9 (8.0) | 9.6 (8.0) | 11.7 (7.9) | 0.0408 |
| MRE (kPa) | 2.7 (1.2) | 2.4 (0.6) | 4.6 (1.9) | <0.0001 |
At-risk MASH defined as a FAST score ≥ 0.67, MAST score of ≥ 0.242, or MEFIB with FIB-4 ≥ 1.6 plus MRE ≥ 3.3 kPa. t test performed on continuous variables presented as mean (SD), Wilcoxon rank sum test on all other continuous variables. Level of significance p< 0.05, n = 525 with VCTE data, n = 524 with CAP, n = 452 with MRE, and n = 452 with MRI-PDFF data. ALT, alanine aminotransferase; AST, aspartate aminotransferase; CAP, controlled-attenuation parameter; FIB-4, fibrosis-4 index; HbA1c, hemoglobin A1c; HOMA-IR, homeostatic model assessment of insulin resistance; INR, international normalized ratio; MRE, magnetic resonance elastography; MRI-PDFF, MRI-proton density fat fraction; MASLD, Metabolic dysfunction-associated steatotic liver disease; T2DM, type 2 diabetes mellitus.
Patients with at-risk MASH were more likely to be younger with elevated liver enzymes, alkaline phosphatase levels, and total bilirubin levels (Table 1). At-risk MASH was also associated with higher rates of advanced fibrosis and cirrhosis. The mean VCTE and MRE scores were higher in patients with at-risk MASH compared to their counterparts (Table 1).
Prevalence of cirrhosis in T2DM
Cirrhosis was defined as MRE ≥ 4.67 kPA or VCTE ≥ 15 kPA21–22. The prevalence of cirrhosis in patients with T2DM was 5.9% (n = 29, Figure 3). The presence of cirrhosis was found to be associated with BMI and obesity (Supplemental table 1). It was not associated with age, sex, ethnicity, duration of diabetes, insulin use, and presence of metabolic syndrome.
Figure 3: Prevalence of MASLD, at-risk MASH, and cirrhosis in adults aged ≥ 50 years with type 2 diabetes.
MASLD, metabolic dysfunction-associated liver disease; MASH, metabolic dysfunction-associated steatohepatitis.
Prevalence of at-risk MASH, MASLD, and cirrhosis in T2DM stratified by obesity
When comparing rates of liver disease in obese versus non-obese patients, we found a significant difference. Among patients with obesity, the prevalence of MASLD, at-risk MASH, and cirrhosis was 77.8%, 15.9%, and 9.0% respectively (Figure 4), significantly higher than in participants without obesity (p-value <.0001, 0.0543, 0.0128, respectively).
Figure 4: Prevalence of MASLD, at-risk MASH, and cirrhosis in adults aged ≥ 50 years with type 2 diabetes stratified by obesity.
MASLD, metabolic dysfunction-associated liver disease; MASH, metabolic dysfunction-associated steatohepatitis.
Discussion
In our prospective study assessing older adults with type 2 diabetes, the prevalence of at-risk MASH was found to be high, posing a higher risk for cirrhosis and liver-related mortality in this population. Prior to our study, the exact quantification of individuals with type 2 diabetes and evidence of at-risk MASH had not been established despite the shared general belief that a large percentage of diabetics have hepatic steatosis and are at high-risk for developing at-risk MASH.
Our analysis further confirmed that diabetics with at-risk MASH are at higher risk for advanced fibrosis (69.4% vs 5.7%) and cirrhosis (43.1% vs 1.1%) when compared to diabetic patients with no evidence of at-risk MASH. More, the presence of obesity amplified the risk of at-risk MASH and cirrhosis. This is especially significant given the substantially higher all-cause mortality among patients with advanced fibrosis and cirrhosis23. Overall, the high burden of undiagnosed liver disease supports the use of non-invasive screening of older diabetic patients.
In context with previous literature
Prior studies have found that patients with type 2 diabetes are at increased risk for developing MASLD, advanced fibrosis, cirrhosis, and hepatocellular carcinoma compared with non-diabetic individuals25. Specifically, in a recent study in patients with MASLD studied with liver biopsies, the presence of T2DM was associated with a 70% increase in relative risk of fibrosis progression25,26. Consistent with this conclusion, our study reveals novel data confirming advanced fibrosis and higher rates of cirrhosis among patients with type 2 diabetes.
Furthermore, in a systematic review and meta-analysis comprising of 14 studies and 17,301 patients with MASLD, all-cause mortality at 1, 5, and 10 years for stage 0 to 2 fibrosis was 0.1%, 3.3%, and 7.7% compared to 0.3%, 20.6%, and 41.5% for stage 4 fibrosis27. This study suggests that all-cause mortality and liver-related mortality increases with fibrosis stage, stressing the importance of screening patients with at-risk MASH to reduce rates of liver related mortality. More, given T2DM has also been found to increase the risk of developing hepatocellular carcinoma, accurate screening tests to guide staging and treatment of severe liver disease become crucial28–30.
Our study further confirms the utility of non-invasive testing in screening this high-risk population. Out of the 530 patients we had, close to 100% had FIB-4 measurements, VCTE. and CAP data. An additional 85% had MRE and MRI-PDFF data. Given the positive predictive value of VCTE remains limited (approximately 55%)5, having most of our patients also undergo MRE and MRI-PDFF (both of which have super diagnostic accuracy with higher specificity and positive predictive value)5, allowed for comprehensive and accurate screening for steatosis and fibrosis in our patients.
Strength and Limitations
Our data sheds light on the high prevalence of MASLD, at-risk MASH, and cirrhosis among type 2 diabetics. This is significant given that patients with MASLD and at-risk MASH are at higher risk for advanced liver fibrosis and cirrhosis. Therefore, it becomes imperative to ensure adequate screening of these patients lower the risk of undiagnosed liver disease. Together, our findings support the implementation of systematic and accurate screening for adults with T2DM and provide essential prospective data regarding the high prevalence of liver disease in this population.
However, there are several limitations to our study. First, it was performed at a single center and is cross-sectional which limits the ability to report on the long-term risk of liver-related morbidity and mortality. Second, although approximately 40% of our participants reported Hispanic ethnicity, we did not have a diverse enough population to detect racial or ethnic differences that could be contributing to the prevalence of advanced fibrosis. Third, we did not have sufficient data to assess the prevalence of T2DM on patients with hepatocellular carcinoma despite this population group being significantly high-risk. Fourth, the change in nomenclature does not necessarily change the overall view on the presence of severe liver disease in type 2 diabetics as these patients remain to be at increased risk for the development of decompensated liver disease and hepatocellular carcinoma.
Supplementary Material
Figure 2:
Stratification of Steatotic Liver Disease. The cutoff for steatotic liver disease is defined by MRI-PDFF > 5%, or if MRI was not available, as CAP > 288 db/min. Advanced fibrosis defined as MRE ≥ 3.63, or if MRE was not available, as VCTE ≥ 8.8. We classified steatotic liver disease between MASLD, MetALD, and ALD. MASLD, Metabolic dysfunction-associated steatotic liver disease; MetALD, Metabolic-alcohol related liver disease; ALD, alcohol related liver disease.
Implications for clinical practice and future research.
Our data supports the implementation of non-invasive liver screening for patients with type 2 diabetes given the high risk of liver disease in this population. Further studies are warranted to assess the degree of complications of advanced fibrosis and cirrhosis as well as prevalence of hepatocellular carcinoma in this high-risk population. More cross-sectional head-to-head trials assessing the efficacy of the different diagnostic criteria of at-risk MASH based on non-invasive modalities would also be of interest to determine the most accurate measure to diagnose at-risk MASH.
FUNDING
VA is supported by NIDDK (K23DK119460). RL receives funding support from NCATS (5UL1TR001442), NIDDK (U01DK061734, U01DK130190, R01DK106419, R01DK121378, R01DK124318, P30DK120515), NHLBI (P01HL147835), and NIAAA (U01AA029019).
ABBREVIATIONS
- MASH
metabolic dysfunction-associated steatohepatitis
- MASLD
Metabolic dysfunction-associated steatotic liver disease
- VCTE
Vibration Controlled Transient Elastography
- MRE
Magnetic resonance elastography
- BMI
body mass index
- AASLD
American Association for the Study of Liver Diseases
- T2DM
Type 2 Diabetes Mellitus
Footnotes
Potential conflict of interests:
RL serves as a consultant to Aardvark Therapeutics, Altimmune, Anylam/Regeneron, Amgen, Arrowhead Pharmaceuticals, AstraZeneca, Bristol-Myer Squibb, CohBar, Eli Lilly, Galmed, Gilead, Glympse bio, Hightide, Inipharma, Intercept, Inventiva, Ionis, Janssen Inc., Madrigal, Metacrine, Inc., NGM Biopharmaceuticals, Novartis, Novo Nordisk, Merck, Pfizer, Sagimet, Theratechnologies, 89 bio, Terns Pharmaceuticals and Viking Therapeutics. In addition his institutions received research grants from Arrowhead Pharmaceuticals, Astrazeneca, Boehringer-Ingelheim, Bristol-Myers Squibb, Eli Lilly, Galectin Therapeutics, Galmed Pharmaceuticals, Gilead, Intercept, Hanmi, Intercept, Inventiva, Ionis, Janssen, Madrigal Pharmaceuticals, Merck, NGM Biopharmaceuticals, Novo Nordisk, Merck, Pfizer, Sonic Incytes and Terns Pharmaceuticals. Co-founder of LipoNexus Inc.
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