Abstract
Background:
There are limited data on the progression of liver stiffness measurement (LSM) by vibration-controlled transient elastography (VCTE) in people with type 2 diabetes (T2DM) versus those without T2DM in biopsy-proven metabolic dysfunction-associated steatotic liver disease (MASLD). We examined LSM progression in participants with T2DM versus those without T2DM in a large, prospective, multicenter cohort study.
Approach and Results:
This study included 1,231 adult participants (62% female) with biopsy-proven MASLD who had VCTEs at least one year apart. LSM progression and regression were defined by a ≥ 20% increase and an upward or downward change, respectively, in the LSM category in the Baveno VII categories for compensated advanced chronic liver disease, compared between participants with T2DM (n=680) versus no T2DM (n=551) at baseline. The mean (±SD) age and BMI were 51.8 (±12.0) years and 34.0 (±6.5) kg/m2, respectively. The median (IQR) time between the first and last VCTE measurements was 4.1(2.5–6.5) years. Participants with T2DM had higher LSM progression at 4-years (12% versus 10%), 6-years (23% versus 16%), and 8-years (50% versus 39%), P=0.04. Using a multivariable Cox proportional hazards model adjusted for multiple confounders, the presence of T2DM remained an independent predictor of LSM progression (adjusted HR 1.35, 95% CI 1.01 – 1.81, P=0.04). T2DM was not associated with LSM regression (P=0.71). Mean HbA1c was significantly associated with LSM progression (P=0.003) and regression (P=0.02).
Conclusion:
Utilizing serial VCTE data from a multicenter study of participants with biopsy-proven MASLD, we demonstrate that T2DM and HbA1c are associated with LSM progression.
Keywords: Metabolic dysfunction-associated steatohepatitis, MASLD, cirrhosis, type 2 diabetes mellitus
Graphical Abstract
INTRODUCTION
Metabolic dysfunction-associated steatotic liver disease (MASLD) affects a third of the world’s adult population and is one of the leading causes of liver-related morbidity and mortality (1–6). MASLD encompasses simple steatosis and metabolic dysfunction-associated steatohepatitis (MASH), the inflammatory form that can progress to cirrhosis, hepatic decompensation, and hepatocellular carcinoma (7–11). The risk of mortality and hepatic decompensation in MASLD increases substantially with the degree of fibrosis (12–16). Liver biopsy is the gold standard for staging hepatic fibrosis but is limited by invasiveness, potential complications, and sampling variability (17). Non-invasive tests of fibrosis, such as vibration-controlled transient elastography (VCTE), are not susceptible to these limitations and are predictive of the histologic fibrosis stage in MASLD (18–20). Recent studies have determined that baseline and dynamic changes in liver stiffness measurements by VCTE may perform as well as histology in predicting liver-related outcomes (21–27).
The prevalence of type 2 diabetes mellitus (T2DM) is rising in parallel with the obesity epidemic. Around two-thirds of individuals with T2DM have MASLD, and around one in six harbor advanced fibrosis (28, 29). A recent study of individuals with paired biopsies determined that T2DM is associated with a higher fibrosis progression rate (16). However, it is unknown if liver stiffness measurements (LSMs) progress faster in people with T2DM compared to people without T2DM. Therefore, we conducted a large, multicenter cohort study within the NASH Clinical Research Network (CRN) consortium to examine LSM progression, and LSM regression, between people with and without T2DM who had available paired VCTE measurements.
METHODS
Study design
This study included adult participants with biopsy-proven MASLD who had paired VCTE measurements at least one year apart, recruited at eight sites in the United States as part of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) sponsored NASH CRN consortium. This multi-center study included participants from the non-interventional registries of the NASH CRN consortium (MASLD Database Study Phases 2, and 3) (NCT01030484, NCT04454463). A total of 1,231 well-characterized participants who underwent serial VCTE assessments were included. All participants provided written informed consent, and the study was approved by the institutional review board (IRB) at each participating site and the data coordinating center (MASLD Database 2), or by the single IRB at Johns Hopkins School of Medicine (MASLD Database 3). Of the 3,343 adult patients who were enrolled as of January 1, 2024, in earlier NASH CRN observational studies or treatment trials that used historical clinical and histological definitions of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) in use at the time, only 7 patients (0.02%) did not have metabolic risk factors. Consequently, we will use the new MASLD, or MASH nomenclature in this paper to describe the NASH CRN patients previously identified as having NAFLD, or NASH, respectively (30).
Inclusion and exclusion criteria
Participants ≥18 years of age with biopsy-proven MASLD and written informed consent were included. Participants were included if they underwent a liver biopsy, had a VCTE measurement, and underwent a VCTE measurement more than one year after the first VCTE. Participants were excluded if they had type 1 diabetes mellitus, had liver disease other than MASLD, had an Alcohol Use Disorders Identification Test questionnaire suggestive of unhealthy alcohol use(31), had hepatocellular carcinoma, or received a liver transplant. We excluded participants with an unreliable VCTE (interquartile range/median ratio (IQR/median) ≤30%) or those with less than 10 valid measurements.
Clinical and laboratory data
Clinical and laboratory data were obtained at the time of the first VCTE and prospectively at 48-week intervals. The presence of T2DM was based on the clinical practice recommendations from the American Diabetes Association and included any of the following criteria: HbA1c ≥ 6.5%; fasting plasma glucose ≥ 126 mg/dL (7.0 mmol/L); plasma glucose ≥ 200 mg/dL (11.1 mmol/L); medical diagnosis of T2DM or use of medications to treat T2DM (32). The metabolic syndrome was defined based on ATP III criteria (having at least 3 of the following 5 factors: impaired fasting glucose ≥110 mg/dL; waist circumference ≥88 cm in women, ≥102 cm in men, triglycerides ≥150 mg/dL, high-density lipoprotein cholesterol <50 mg/dL in women, 40 mg/dL in men, systolic BP ≥130 mmHg or diastolic BP ≥85 mmHg (33).
Histological assessment
All participants underwent a liver biopsy at baseline. Liver histology assessment for grade and stage was conducted per NASH CRN protocol as a consensus review of glass slides for each feature of the MASLD activity score (NAS) and stage. The biopsy specimens were examined by the NASH CRN central pathology committee, which comprised at least three pathologists during each assessment. Pathologists were unaware of clinical data or the sequence of the biopsy at the time of review. Hematoxylin and eosin and trichrome stained slides were reviewed for NAS components (steatosis, lobular activity, and ballooning degeneration) and fibrosis stage per NASH CRN criteria and practice (34). Separately, a diagnosis of MASH, borderline MASH, MASLD not MASH, or not MASLD was made based on recognition of the distinctive features of steatohepatitis independent of the NAS. (35)
Liver stiffness measurements by VCTE
All participants underwent a baseline measurement of liver stiffness by VCTE, and repeat measurement of liver stiffness by VCTE on a protocolized, yearly basis. All participants had at least three hours of fasting before LSM. The FibroScan 502 Touch with two probes, M+ and XL+, was provided by Echosens as part of a clinical trials agreement between Echosens and the National Institute of Diabetes and Digestive and Kidney Diseases, and used at each of the participating sites to measure liver stiffness. The FibroScan 502 Touch was replaced by the 630 Expert with SmartExam in November 2022. The type of probe utilized was chosen based on the automatic probe selection tool of the FibroScan 502 Touch operating software. We used the first and latest VCTEs for comparison.
Outcome measures
The primary outcome was LSM progression, defined by a ≥ 20% increase (22–24, 36) and an upward change in the LSM category (<10 kPa, 10–14.9 kPa, 15.0–19.9 kPa, 20.0–24.9 kPa, ≥25.0 kPa) defined by Baveno VII and AASLD practice guidance, compared between T2DM versus non-T2DM at baseline (37, 38). The secondary outcome was LSM regression, defined by a ≥ 20% decrease and a downward change in the LSM category, compared between participants with T2DM versus participants without T2DM. The threshold of a 20% increase or decrease was chosen as multiple studies have determined a strong association with a 20% change in LSM and liver-related outcomes (22–24, 36).
Statistical analyses
Descriptive statistics of participant demographic, laboratory, histological, and imaging characteristics at baseline were presented and dichotomized by the presence of T2DM at baseline. Baseline categorical variables were compared with chi-square, and continuous variables were compared using a t-test or Wilcoxon two-sample test where appropriate Survival analysis was conducted to evaluate time-to-LSM progression. The Cox proportional hazards model was used to evaluate the hazard ratio (HR) for LSM progression and regression, compared between participants with T2DM at baseline versus participants without T2DM at baseline, adjusted for age, sex, body mass index (BMI), and Hispanic ethnicity. Participants with baseline liver stiffness measurement ≥ 25.0 kPa were excluded from the analysis for LSM progression since there can be no further progression. Participants with baseline liver stiffness <10 kPa were excluded from the analysis for LSM regression since there can be no further regression to a lower Baveno category. A sensitivity analysis was performed to adjust for the presence of baseline LSM ≥ 15 kPa at baseline (38). The Cox proportional hazards model was used to evaluate the association of mean HbA1c with LSM progression, and regression. The proportional hazards assumptions were met for both the progression and regression models. Statistical significance was defined as a two-tailed P value of ≤0.05. Analyses were conducted with the use of SAS software, version 9.4 (SAS Institute), and Stata software, version 15.1 (StataCorp).
RESULTS
Characteristics of the study population
A total of 2,930 participants were enrolled between 2009 through 2022 (Figure 1). After applying the inclusion and exclusion criteria (Figure 1), a total of 1,231 adult participants with biopsy-confirmed MASLD (62% female) and available paired VCTEs were included in this study. The mean (±SD) age and BMI were 51.8 (±12.0) years and 34.0 (±6.5) kg/m2, respectively. Most participants (85%) were White, and 11% were Hispanic. A total of 601 participants received a biopsy within 6 months of VCTE, and the number with baseline fibrosis stages 0, 1, 2, 3, and 4 was 144 (24%),128 (21%),112 (19%), 141(23%), and 76 (13%), respectively (Supplemental Table 1). The median (IQR) baseline liver stiffness by VCTE was 7.8 (5.4–12.5) kPa, and the median (IQR) time between VCTE measurements was 4.1 (2.5–6.5) years (Table 1). Participants with T2DM at baseline were older (53.5 years versus 49.7 years, P<0.001), had a greater proportion of females (69% versus 54%, P<0.001), had higher BMI (35.0 kg/m2 versus 32.7 kg/m2, P<0.001), and were more likely to have the metabolic syndrome (75% versus 41%, P<0.001). Participants with T2DM had a higher baseline median LSM by VCTE (9.3kPa versus 6.3kPa, P<0.001). There was a longer time between the first and last VCTE measurements among participants with T2DM versus those without T2DM (4.2 years versus 3.8 years, P=0.003).
Figure 1.
Study flow diagram.
Table 1.
Baseline characteristics of participants with paired VCTE, stratified by the presence of type 2 diabetes at baseline
| Variable | Overall (N=1,231) |
No diabetes (N=551) |
Diabetes (N=680) |
|---|---|---|---|
| Demographics | s | ||
| Age — yr | 51.8 (12.0) | 49.7 (12.9) | 53.5 (10.8) |
| Sex — no. (%) | |||
| Male | 464 (38%) | 251 (46%) | 213 (31%) |
| Female | 764 (62%) | 298 (54%) | 466 (69%) |
| Race — no. (%) | |||
| White | 1,014 (85%) | 451 (85%) | 563 (85%) |
| Other or not reported | 174 (15%) | 78 (15%) | 96 (15%) |
| Hispanic ethnic group | |||
| Yes | 130 (11%) | 78 (14%) | 52 (8%) |
| No | 1,098 (89%) | 471 (86%) | 627 (92%) |
| Baseline histologic features | |||
| Days between biopsy and VCTE – Median (IQR) | 42.5 (23.0, 68.0) | 42.0 (24.5, 65.5) | 43.0 (22.0, 69.0) |
| NAFLD activity score | 4.3 (1.6) | 3.9 (1.6) | 4.6 (1.6) |
| Fibrosis stage – N (%) | |||
| Stage 0 | 144 (24%) | 108 (37%) | 36 (12%) |
| Stage 1 | 128 (21%) | 68 (23%) | 60 (19%) |
| Stage 2 | 112 (19%) | 52 (18%) | 60 (19%) |
| Stage 3 | 141 (23%) | 42 (14%) | 99 (32%) |
| Stage 4 | 76 (13%) | 23 (8%) | 53 (17%) |
| MASH diagnosis | |||
| Definite/borderline MASH | 473 (79%) | 195 (67%) | 278 (90%) |
| MASLD, not MASH | 128 (21%) | 98 (33%) | 30 (10%) |
| Biopsy specimen length — mm | 23.7 (9.8) | 23.0 (9.5) | 24.5 (10.0) |
| Body-mass index | |||
| Mean (SD) | 34.0 (6.5) | 32.7 (6.3) | 35.0 (6.5) |
| Distribution — no. (%) | |||
| <25 | 63 (5%) | 40 (7%) | 23 (3%) |
| 25 to <30 | 300 (24%) | 163 (30%) | 137 (20%) |
| 30 to <35 | 396 (32%) | 180 (33%) | 216 (32%) |
| ≥35 | 470 (38%) | 168 (30%) | 302 (45%) |
| Comorbidities— no. (%) | |||
| Hypertension | 741 (60%) | 254 (46%) | 487 (72%) |
| Coronary artery disease | 51 (4%) | 14 (3%) | 37 (5%) |
| Metabolic syndrome | 712 (60%) | 223 (41%) | 489 (75%) |
| Laboratory results – Median (IQR) | |||
| ALT — U/L | 44.0 (28.0, 67.0) | 45.0 (28.0, 70.0) | 43.0 (29.0, 65.0) |
| AST — U/L | 35.0 (25.0, 51.0) | 34.0 (25.0, 51.0) | 36.0 (26.0, 51.0) |
| ALP — U/L | 74.5 (60.0, 92.0) | 72.0 (58.0, 90.0) | 76.0 (62.0, 95.0) |
| Total bilirubin (SD) — mg/dl – mean (SD) | 0.7 (0.4) | 0.7 (0.4) | 0.7 (0.4) |
| INR – mean (SD) | 1.0 (0.1) | 1.0 (0.1) | 1.0 (0.2) |
| Serum creatinine — mg/dl | 0.80 (0.70, 0.90) | 0.80 (0.70, 1.00) | 0.80 (0.70, 0.90) |
| eGFR — ml/min/1.73 m2 | 92.29 (77.89, 102.51) | 91.1 (80.4, 101.9) | 93.2 (76.4, 103.0) |
| Serum albumin — g/dl | 4.4 (4.2, 4.6) | 4.4 (4.2, 4.7) | 4.3 (4.1, 4.5) |
| Platelets per μL | 222.0 (179.0, 266.0) | 223.0 (187.0, 266.0) | 222.0 (175.0, 265.0) |
| FIB-4 | 1.7 (1.4) | 1.6 (1.5) | 1.8 (1.3) |
| HbA1c — % | 6.4 (1.4) | 5.5 (0.4) | 7.0 (1.5) |
| Fasting glucose – mg/dL | 115.6 (41.9) | 94.4 (11.2) | 132.7 (49.2) |
| Fasting insulin – ng/mL | 26.6 (30.3) | 20.5 (18.7) | 31.5 (36.4) |
| HOMA-IR | 8.3 (12.3) | 4.9 (5.2) | 11.1 (15.3) |
| Total cholesterol - mg/dL | 179.3 (42.0) | 182.4 (41.2) | 176.8 (42.5) |
| HDL cholesterol – mg/dL | 45.6 (12.5) | 47.2 (13.2) | 44.2 (11.8) |
| LDL cholesterol – mg/dL | 102.6 (35.9) | 107.3 (34.8) | 98.7 (36.4) |
| Triglycerides – mg/dL | 142.0 (104.0, 204.0) | 130.0 (96.0, 175.0) | 152.0 (115.0, 219.0) |
| LSM (kPa) by transient elastography | |||
| Median (IQR) | 7.8 (5.4, 12.5) | 6.3 (4.8, 9.6) | 9.3 (6.1, 15.0) |
| Median time between transient elastography measurements (IQR) — yr | 4.1 (2.5, 6.5) | 3.8 (2.4, 6.4) | 4.2 (2.6, 6.6) |
Values are medians (IQR) unless otherwise noted. Percentages may not total 100 because of rounding.
Race was reported by the patient.
Abbreviations: ALP, alkaline phosphatase; ALT, alanine transaminase; AST, aspartate transaminase; eGFR, estimated glomerular filtration rate; FIB-4, Fibrosis-4 Index; HbA1c, Hemoglobin A1c; HDL, high-density lipoprotein; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; INR, international normalized ratio; IQR interquartile range; LDL, low-density lipoprotein; LSM, liver stiffness measurement; MASLD, metabolic dysfunction-associated steatotic liver disease; MASH, metabolic dysfunction-associated steatohepatitis; NAFLD, nonalcoholic fatty liver disease; SD, standard deviation
Body-mass index is the weight in kilograms divided by the square of the height in meters. A body-mass index of less than 25 indicates normal weight, of 25 to less than 30 overweight, 30 to less than 35 obese, and 35 or above morbid obesity.
The NAFLD activity score was assessed on a scale of 0 to 8, with higher scores indicating more severe disease; the components of this measure are steatosis (assessed on a scale of 0 to 3), lobular inflammation (assessed on a scale of 0 to 3), and hepatocellular ballooning (assessed on a scale of 0 to 2).
The eGFR was calculated with the use of the Chronic Kidney Disease Epidemiology Collaboration formula.
LSM progression in participants with T2DM versus participants without T2DM
The transitions of the categories LSMs are summarized in Supplemental Table 2. Among participants with a baseline liver biopsy, the LSM at first VCTE is provided in Supplemental Table 2. Overall, 18% of participants had an increase in the Baveno LSM category, 65% of participants had no change in the LSM category, and 17% of participants had a reduction in the LSM category.
Participants with T2DM at baseline had significantly higher LSM progression by ≥1 LSM category at 4-years (12% [95% CI 9–15] versus 10% [95% CI 8–14]), 6-years (23% [95% CI 19–28] versus 16% [95% CI 12–21]) and 8-years (50% [95% CI 42–59] versus 39% [CI 29–51]), P=0.04, after adjustment for age, sex, ethnicity/race, and body mass index, compared with participants without T2DM at baseline (Figure 2).
Figure 2.
Liver stiffness measurement progression in metabolic dysfunction-associated steatotic liver disease, among participants with T2DM versus participants without T2DM.
Abbreviation: T2DM, type 2 diabetes mellitus
Association of T2DM with LSM progression
In unadjusted analysis, T2DM at baseline was associated with LSM category progression (HR 1.47, 95% CI 1.11 – 1.95, P =0.008) (Table 2). After multivariable adjustment for age, sex, BMI, and race/ethnicity, the presence of T2DM at baseline remained statistically significant and an independent predictor of LSM category progression (adjusted HR 1.35, 95% CI 1.01 – 1.81, P=0.04). A separate model adjusted for baseline LSM ≥ 15 kPa determined similar findings (adjusted HR 1.37, 95% CI 1.03 – 1.83, P= 0.03).
Table 2.
Predictors of liver stiffness measurement progression and regression
| Predictors of liver stiffness progression | Hazard Ratio (95% CI) | P-value |
| T2DM, unadjusted | 1.47 (1.11 – 1.95) | 0.008 |
| T2DM, adjusted for age and sex | 1.47 (1.10 – 1.96) | 0.009 |
| T2DM, adjusted for age, sex, BMI, and Hispanic race/ethnicity | 1.35 (1.01 – 1.81) | 0.04 |
| Predictors of liver stiffness regression | Hazard Ratio (95% CI) | P-value |
| T2DM, unadjusted | 0.92 (0.66 – 1.27) | 0.61 |
| T2DM, adjusted for age and sex | 0.95 (0.68 – 1.31) | 0.75 |
| T2DM, adjusted for age, sex, BMI, and Hispanic race/ethnicity | 0.94 (0.68 – 1.31) | 0.71 |
Abbreviation: T2DM, type 2 diabetes mellitus
Association of mean HbA1c with LSM progression
Over follow-up, the median (IQR) number of HbA1c measurements was 4 (3–6) in participants with T2DM, and 4 (2–6) in participants without T2DM. In unadjusted analysis, mean HbA1c was associated with LSM category progression (HR 1.20, 95% CI 1.08 – 1.33, P =0.001) (Table 3). After multivariable adjustment for age, sex, BMI, and race/ethnicity, mean HbA1c remained statistically significant and an independent predictor of LSM progression (adjusted HR 1.18, 95% CI 1.06 – 1.32, P=0.003). A separate model adjusted for baseline LSM ≥ 15 kPa determined similar findings (adjusted HR 1.16, 95% CI 1.04 – 1.30, P= 0.009).
Table 3.
Mean HbA1c as a predictor of liver stiffness measurement progression and regression
| Predictors of liver stiffness progression | Hazard Ratio (95% CI) | P-value |
| HbA1c, unadjusted | 1.20 (1.08 – 1.33) | 0.001 |
| HbA1c, adjusted for age and sex | 1.20 (1.08 – 1.33) | 0.001 |
| HbA1c, adjusted for age, sex, BMI, and Hispanic race/ethnicity | 1.18 (1.06 – 1.32) | 0.003 |
| Predictors of liver stiffness regression | ||
| HbA1c, unadjusted | 0.84 (0.73 – 0.97) | 0.02 |
| HbA1c, adjusted for age and sex | 0.85 (0.74 – 0.98) | 0.03 |
| HbA1c, adjusted for age, sex, BMI, and Hispanic race/ethnicity | 0.84 (0.72 – 0.97) | 0.02 |
Abbreviation: HbA1c, hemoglobin A1c
LSM regression in participants with T2DM versus participants without T2DM
Participants with T2DM at baseline had similar LSM regression at 4-years (29% [95% CI 23–35] versus 26% [95% CI 18–36]), 6-years (42% [95% CI 35–49] versus 39% [95% CI 29–51]), and 8-years (77% [95% CI 67–86] versus 82% [95% CI 68–92]), P=0.71, after adjustment for age, sex, ethnicity/race, and body mass index, compared with participants without T2DM at baseline (Figure 3).
Figure 3.
Liver stiffness measurement regression in metabolic dysfunction-associated steatotic liver disease, among participants with T2DM versus participants without T2DM.
Abbreviation: T2DM, type 2 diabetes mellitus
Association of T2DM with LSM regression
In unadjusted analysis, T2DM at baseline was not associated with LSM category regression (HR 0.92, 0.66 – 1.27, P =0.61). After multivariable adjustment for age, sex, BMI, and race/ethnicity, the presence of T2DM at baseline was not associated with LSM regression (adjusted 0.94, 95% CI 0.68 – 1.31), P=0.71). A separate model adjusted for baseline LSM ≥ 15 kPa determined similar findings (adjusted HR 0.90, 95% CI 0.64 – 1.24, P= 0.51).
Association of mean HbA1c with LSM regression
In unadjusted analysis, a higher HbA1c was associated with a lower likelihood of LSM category regression (HR 0.84, 95% CI 0.73 – 0.97, P =0.02) (Table 3). After multivariable adjustment for age, sex, BMI, and race/ethnicity, mean HbA1c remained statistically significant and an independent predictor of a lower likelihood of LSM regression (adjusted HR 0.84, 95% CI 0.72 – 0.97, P=0.02). A separate model adjusted for baseline LSM ≥ 15 kPa determined similar findings (adjusted HR 0.82, 95% CI 0.71 – 0.95, P= 0.009).
Subgroup and sensitivity analyses
There was an increase in the proportion with AGILE 3+ ≥0.679 in participants with T2DM at follow-up (42.1% versus 49.3%, P=0.03), but not in participants without T2DM, while there were no significant changes in AGILE 4 categories (P=0.39) (Supplemental Table 4). We determined consistent findings in the association of T2DM with LSM progression, even after adjustment for medication classes (adjusted HR 1.46, 95% CI 1.06–2.00, P=0.02) (Supplemental Table 5). The proportion of LSM progression in participants with T2DM and stage 3 or 4 fibrosis was similar to that seen in participants with T2DM and stage 0–2 fibrosis (55% versus 45%, p=0.44). The proportion of LSM regression in participants with T2DM and stage 3 or 4 fibrosis was larger than that seen in participants with T2DM and stage 0–2 fibrosis (69% versus 31%, p<0.001). Among participants with T2DM, changes in aspartate aminotransferase, alanine aminotransferase, HbA1c, BMI, and baseline HOMA-IR were associated with LSM progression, while changes in aspartate aminotransferase and BMI were associated with LSM regression (Supplemental Table 6). The impact of T2DM on LSM progression remained consistent when adjusted for other features of the metabolic syndrome, such as arterial pressure, high-density lipoprotein, and triglycerides (Supplemental Table 7). The presence of pre-diabetes (defined by any of the following: hemoglobin A1C of 5.7–6.4%; fasting blood glucose of 100–125 mg/dL; oral glucose tolerance test two-hour blood glucose of 140–199 mg/dL) (39) was not associated with LSM progression (HR 1.06, 95% CI 0.67 – 1.67, p=0.80), compared to participants without pre-diabetes or T2DM.
DISCUSSION
Main findings
In this large, prospective, multicenter center study of well-characterized participants with paired VCTEs within the NASH CRN consortium, we determined that participants with T2DM had higher LSM progression at 4-years (12% versus 10%), 6-years (23% versus 16%), and 8-years (50% versus 39%) compared to those without T2DM. T2DM remained a significant predictor of LSM progression, even after adjustment for potential confounders, and baseline LSM. T2DM was not associated with LSM regression. Mean HbA1c was an independent predictor of LSM progression and regression.
In context with current literature
Emerging data suggest that dynamic changes in LSM are associated with liver-related outcomes (22, 23, 40). However, it was previously unknown whether LSM progresses or regresses at different rates in people with and without T2DM. The current study fills this knowledge gap and demonstrates that T2DM is associated with LSM progression, possibly related to the impact of hyperinsulinemia on hepatic stellate cells (41). A recent study of participants with MASLD and paired biopsies demonstrated a faster fibrosis progression rate in people with T2DM but did not determine a significant association between baseline HbA1c and fibrosis progression or regression (16). By contrast, the current study showed a significant association between HbA1c and LSM progression and regression, possibly related to the larger sample size, and the use of mean HbA1c rather than baseline HbA1c. These build on the data from a previous study that determined a significant association between the mean HbA1c over the years preceding a liver biopsy, and the odds of histologic fibrosis (42). A previous study demonstrated that a longer duration of T2DM was associated with a higher risk of liver-related events and all-cause mortality (43). We built upon these data by demonstrating that glycemic control is associated with dynamic changes in LSM progression, which may be helpful for risk stratification in people with T2DM.
Strengths and limitations
The strengths of this study include its prospective nature, large sample size, multicenter study design, and well-characterized participants with centrally read liver biopsies. However, it is not without limitations. The study was conducted in eight sites in the United States, and the majority of the participants were White, hence it is unclear if these data are generalizable to other regions. It is possible that selection bias may have occurred, hence real-world data may be helpful to validate these findings. There were limited participants with extended follow-up beyond six years, hence the data requires cautious interpretation. There was a longer time follow-up time between the first and last VCTE measurements in participants with T2DM compared to those without T2DM, and there was a difference in baseline LSM, however, we accounted for time-to-event in the Cox proportional hazards model and adjusted for baseline LSM ≥ 15 kPa. All study coordinators underwent standardized training by Echosens and were certified before conducting VCTEs, although we lack data on the experience of individual technicians.
Implications for clinical care and research
The presence of T2DM, and mean HbA1c, was associated with LSM progression. In contrast, a lower mean HbA1c was associated with LSM regression. These data are useful for the design of clinical trials and suggest that glycemic control may be associated with disease progression in MASLD. Further research is required to determine if improvements in glycaemic control among patients with baseline suboptimal HbA1c lead to regression in LSM.
Conclusion
In this large, prospective, multicenter cohort study of well-characterized participants with biopsy-proven MASLD, we demonstrate that LSM progresses faster in T2DM compared to people without T2DM. T2DM remained a strong and independent predictor for LSM progression, even after adjustment for multiple confounders. Mean HbA1c was independently associated with LSM progression and regression, suggesting that glycemic control may be an important determinant of disease progression in MASLD.
Supplementary Material
Grant support:
The Nonalcoholic Steatohepatitis Clinical Research Network (NASH CRN) is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (grants U01DK061713, U01DK061718, U01DK061728, U01DK061732, U01DK061734, U01DK061737, U01DK061738, U01DK061730, U24DK061730). Additional support is received from the National Center for Advancing Translational Sciences (NCATS) (grants UL1TR000439, UL1TR000436, UL1TR000006, UL1TR000448, UL1TR000100, UL1TR000004, UL1TR000423, UL1TR002649). This research was supported in part by the Intramural Research Program of the NIH, National Cancer Institute. FibroScan® 502 Touch and 630 Expert systems were provided to the NASH CRN Investigators by Echosens™ through a Clinical Trial Agreement with the NIDDK. The authors thank the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) for its support of the NASH CRN and this research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
RL receives funding support from NCATS (5UL1TR001442), NIDDK (U01DK061734, U01DK130190, R01DK106419, R01DK121378, R01DK124318, P30DK120515), NHLBI (P01HL147835), John C Martin Foundation (RP124) and NIAAA (U01AA029019).
D.H. receives funding support from the Singapore Ministry of Health’s National Medical Research Council (MOH-001370) and the NUHS Clinician Scientist Programme (NCSP2.0/2023/NUHS/DH).
Conflicts of Interest
Daniel Q. Huang advises Gilead and Roche. Kris V. Kowdley consults, is on the speakers’ bureau, and received grants from Gilead, Intercept, Ipsen, and Madrigal. He consults and received grants from 89bio, CymaBay, GENFIT, GlaxoSmithKline, Mirum, NGM, and Zydus. He consults and owns stock in Inipharm. He consults for HighTide. He is on the speakers’ bureau for AbbVie. He received grants from Boston, Corcept, Hanmi, Janssen, Novo Nordisk, Pfizer, Pliant, Terns, and Viking. He has other interests with UpToDate. Norah A. Terrault consults for Moderna. She received grants from Durect, Gilead, GlaxoSmithKline, Helio Health, ImmunoCore, and Roche-Genetech. She has other interests with Elsevier. Anna Mae Diehl consults and received grants from Allergan, Gilead, Glympse, Merck, and Novartis. She consults for Alderya, Aria, Casma, Filcitrine, Generon, Pfizer, RAPT, and SunBio. She received grants from AstraZeneca, Boehringer Ingelheim, Celgene, Enyo, Excella Health, Galmed, GlaxoSmithKline, Hanmi, HeptaBio, Intercept, Inventiva, Madrigal, NGM Bio, Novo Nordisk, Poxel, Tune, and Viking. Naga Chalasani consults for Altimmune, Foresite, GlaxoSmithKline, Madrigal, Merck, Pfizer, and Zydus. He received grants from DSM and Exact. He owns stock in Avant Sante. Brent A. Neuschwander-Tetri consults and advises Akero, Arrowhead, Boehringer Ingelheim, Corcept, GlaxoSmithKline, Hepion, HistoIndex, Madrigal, Merck, Mirum, Sagimet, and Senseion. He received grants from Bristol Myers Squibb, HighTide, Intercept, Inventiva, and Madrigal. He owns stock in HepGene and Heptabio. Arun J. Sanyal consults and received grants from AstraZeneca, Bristol Myers Squibb, Gilead, Merck, Novartis, and Salix. He consults for 89 Bio, Alnylam, Akero, Boehringer Ingelheim, Corcept, Eli Lilly, GENFIT, HemoShear, HistoIndex, Jannsen, Novo Nordisk, Path AI, Pfizer, Poxel, Regeneron, Robira, Takeda, and Terns. He received grants from Intercept, Mallinckrodt, Shire, and Tobira. He owns stock in Akarna, Durect, Galmed, GENFIT, Inversago, and Tiziana. He has other interests with Elsevier and UpToDate. Rohit Loomba consults and received grants from Arrowhead, AstraZeneca, Eli Lilly, Gilead, Intercept, Inventiva, Ionis, Janssen, Madrigal, Merck, Novo Nordisk, Pfizer, and Terns. He consults for 89bio, Aardvark, Altimmune, Cascade, Glympse, Inipharm, Lipidio, NeuroBo, Sagimet, Takeda, and Viking. He received grants from Boehringer Ingelheim, Bristol Myers Squibb, Galectin, Hanmi, and Sonic Incytes. He owns stock in Sagimet. He is employed by LipoNexus. The remaining authors have no conflicts to report.
Abbreviations:
- MASH
metabolic dysfunction-associated steatohepatitis
- NAFLD
metabolic dysfunction-associated steatotic liver disease
- T2DM
type 2 diabetes mellitus
Footnotes
Transcript profiling: Not applicable
Writing assistance: Not applicable
Data transparency statement: Data will not be made publicly available.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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