Abstract
Background
Dynamic changes in non-invasive tests, such as changes in alanine aminotransferase (ALT) and MRI proton-density-fat-fraction (MRI-PDFF), may help to detect metabolic dysfunction-associated steatohepatitis (MASH) resolution, but a combination of non-invasive tests may be more accurate than either alone. We developed a novel non-invasive score, the MASH Resolution Index, to detect the histological resolution of MASH.
Methods
This study included a derivation cohort of 95 well-characterised adult participants (67% female) with biopsy-confirmed MASH who underwent contemporaneous laboratory testing, MRI-PDFF and liver biopsy at two time points. The primary objective was to develop a non-invasive score to detect MASH resolution with no worsening of fibrosis. The most predictive logistic regression model was selected based on the highest area under the receiver operating curve (AUC), and the lowest Akaike information criterion and Bayesian information criterion. The model was then externally validated in a distinct cohort of 163 participants with MASH from a clinical trial.
Results
The median (IQR) age and body mass index were 55 (45–62) years and 32.0 (30–37) kg/m2, respectively, in the derivation cohort. The most accurate model (MASH Resolution Index) included MRI-PDFF, ALT and aspartate aminotransferase. The index had an AUC of 0.81 (95% CI 0.69 to 0.93) for detecting MASH resolution in the derivation cohort. The score calibrated well and performed robustly in a distinct external validation cohort (AUC 0.83, 95% CI 0.76 to 0.91), and outperformed changes in ALT and MRI-PDFF.
Conclusion
The MASH Resolution Index may be a useful score to non-invasively identify MASH resolution.
INTRODUCTION
Metabolic dysfunction-associated steatohepatitis (MASH) is the progressive form of metabolic dysfunction-associated steatotic liver disease (MASLD) that can progress to cirrhosis, hepatic decompensation and hepatocellular carcinoma (HCC).1–7 The global prevalence of MASH approaches 14% in middle-aged US populations8 9 and is projected to increase exponentially worldwide.10 However, there are no US Food and Drug Administration-approved therapies for MASH, representing a major unmet need in the field.11
The resolution of MASH is an accepted regulatory endpoint for subpart H approval of therapies developed for MASH but requires an invasive liver biopsy for its assessment. Liver biopsy may have serious side effects and is a major barrier to enrolment in clinical trials.12 In addition, liver biopsy is associated with sampling variability, and suboptimal interreader and intrareader variability.13 14 There is a major unmet need to non-invasively determine the resolution of MASH in a precise and reproducible manner. Recent studies have determined that MRI proton-density-fat fraction (PDFF) provides an accurate, non-invasive, quantitative and precise estimation of liver fat content and may be superior to histology in quantifying changes in liver fat.15–19 A ≥30% decline in MRI-PDFF (MRI-PDFF response), a ≥17 U/L decrease in alanine aminotransferase (ALT) (ALT response) and a combined MRI-PDFF and ALT response are associated with higher odds of histological response, but the area under the receiver operator curves (AUCs) of these non-invasive tests for detecting MASH resolution are suboptimal.20–24 We hypothesised that a score comprising a combination of baseline and dynamic changes in MRI-PDFF and ALT, along with a third clinical variable would be more accurate for identifying MASH than individual non-invasive tests in isolation. Using two well-characterised, independent cohorts of adults with MASH who had a paired liver biopsy and contemporaneous MRI-PDFF assessment, we developed the MASH Resolution Index, a non-invasive score to detect MASH resolution and externally validated it in a distinct prospective cohort.
METHODS
Study design
This is a longitudinal study derived from two independent, well-characterised cohorts with biopsy-proven MASH at baseline. This study included a derivation cohort comprising 95 consecutive, well-phenotyped adult participants who underwent detailed standardised research visits that included history, physical examination, biochemical validation, and paired liver biopsy and MRI-PDFF at the MASLD research centre, University of California, San Diego (UCSD) from March 2010 to November 2020. This cohort included participants in placebo-controlled drug trials of ezetimibe, sitagliptin, colesevelam, cenicriviroc, obeticholic acid, MSDC-0602K, elafibranor and aramchol, as well as participants who received a follow-up biopsy as part of standard medical care. None of the participants in the derivation cohort received pegozafermin. The validation cohort comprised 163 participants with MASH and stage 2 or stage 3 fibrosis from a randomised phase 2b, multicentre, placebo-controlled trial of pegozafermin, a fibroblast growth factor 21 (FGF21) analogue for the treatment of MASH.25 Participants received either pegozafermin or placebo and underwent two sets of contemporaneous liver biopsies and MRI-PDFF spaced 24 weeks apart.25
Inclusion and exclusion criteria
Participants ≥18 years of age with biopsy-proven MASH were included. Participants were included if lab testing and MRI-PDFF were performed contemporaneously with liver biopsy, and they had a subsequent liver biopsy and contemporaneous lab testing and MRI-PDFF (window between biopsy, lab testing and MRI-PDFF≤90 days at each time point).
Participants were assessed for liver disease other than MASLD, and those meeting any of the following criteria were excluded from the study: significant alcohol consumption (≥14 drinks/week for men or ≥7 drinks/week for women) within the previous 2-year period; clinical or laboratory evidence of secondary causes associated with hepatic steatosis including nutritional disorders, HIV infection and use of steatogenic drugs such as amiodarone, glucocorticoids, methotrexate, l-asparaginase and valproic acid; active substance use; underlying liver disease other than MASLD such as viral hepatitis (assessed with serum hepatitis B surface antigen and hepatitis C RNA assays), haemochromatosis, Wilson’s disease, alpha-1 antitrypsin deficiency, glycogen storage disease, autoimmune hepatitis and cholestatic or vascular liver disease; any contraindications to MRI including metallic implants, claustrophobia and body circumference exceeding the imaging chamber capacity; known allergy to any gadolinium agent; bilirubin>3 mg/dL; known or suspected nephrogenic systemic sclerosis; pregnancy.
Histological evaluation
All participants underwent a liver biopsy at baseline, followed by a second liver biopsy. Histological assessment in the derivation cohort was performed by a single, expert liver pathologist, while assessment in the validation cohort was performed by a central panel of three expert liver pathologists. The pathologists in the derivation and validation cohorts were blinded to the clinical/imaging data and the sequence of biopsies. The biopsies were scored based on the NASH CRN Histologic Scoring System.26 The nonalcoholic fatty liver disease (NAFLD) activity score (NAS), a summary score ranging from 0 to 8 that includes steatosis (0–3), lobular inflammation (0–3) and ballooning (0–2), was scored for each participant. Fibrosis was scored on a scale of 0–4.
Clinical research evaluation visit
All participants in the derivation cohort underwent a standardised clinical evaluation at baseline, including a detailed history, anthropometric exam and laboratory investigations at the MASLD Research Center, UCSD. A trained coordinator documented information including age, sex, height, weight, body mass index (BMI) and ethnicity. Alcohol intake history was verified with the Alcohol Use Disorders Identification Test and the Skinner questionnaire. Other causes of liver diseases and hepatic steatosis were ruled out based on history and laboratory tests. Clinical investigators were blinded to imaging results.
Advanced imaging assessment
Advanced MRI-based phenotyping for MRI-PDFF was performed by the UCSD Liver Imaging Group using a 3T research scanner (GE Signa EXCITE HDxt; GE Healthcare, Waukesha, Wisconsin, USA). The MRI protocols have been described in detail.27–29 The imaging analysts were blinded to clinical and histological data.
Primary objective and definitions
The primary objective of this study was to determine a non-invasive score to detect MASH resolution, defined as absent or mild inflammation (lobular inflammation score of 0 or 1), no hepatocyte ballooning (ballooning score of 0), without worsening of fibrosis.
MRI-PDFF response was defined as a ≥30% relative decline in MRI-PDFF.21, 30 ALT response was defined as a decrease in ALT level of ≥17 U/L.22 A combined MRI-PDFF and ALT response was defined as those who met both of these criteria.24
Statistical analysis
Descriptive statistics of participant demographic, laboratory, histological and imaging characteristics were presented at baseline. Baseline categorical variables were compared with χ2, and continuous variables were compared using a t-test or Wilcoxon two-sample test where appropriate. Univariable and multivariable logistic regression analyses were performed for factors associated with MASH resolution in the derivation cohort. A decline in ALT and a decline in MRI-PDFF are known to be strongly associated with MASH resolution.21 22 The models included baseline ALT and change in ALT, baseline MRI-PDFF and percentage change in PDFF, and evaluated one of the following variables known to associate with histological response in MASH based on data from a secondary analysis of a clinical trial22: baseline triglyceride level, baseline international normalised ratio (INR) and baseline aspartate aminotransferase (AST) levels. Calibration was assessed using calibration plots and a smoothing technique based on locally estimated scatterplot smoothing. The discrimination of the models was assessed by AUC. The model with the higher AUC and lower Akaike information criterion (AIC) and Bayesian information criterion (BIC) value in the derivation cohort was selected as the final model. Cut points for sensitivity (≥90%) and specificity (≥90%) were developed in the derivation cohort. The model was externally validated in a separate cohort derived from a recent randomised controlled trial in participants with MASH.25 AUCs were compared using the Delong test. Statistical significance was defined as a two-tailed p≤0.05. All statistical analyses were performed on a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria) and SAS V.9.4.
RESULTS
Characteristics of the study population
This study included a derivation cohort of 95 consecutive eligible participants (67% female) with MASH who underwent paired liver biopsies and MRI-PDFF assessments at two time points (table 1). The median (IQR) age and BMI were 55.0 (44.5–61.5) years and 32.0 (30.0–37.0) kg/m2, respectively. The median (IQR) interval between liver biopsies was 1.3 (0.65–2.62) years. At follow-up biopsy, 18 (18.9%) participants had MASH resolution. The validation cohort comprised 163 participants (64% female) with MASH and stage 2 (n=57) or stage 3 (n=106) liver fibrosis from a randomised trial, who had two sets of paired liver biopsies and MRI-PDFF measurements 24 weeks apart (table 2). The median (IQR) age and BMI of participants in the validation cohort were 56.0 (48.0–62.0) years and 36.5 (32.2–40.2) kg/m2, respectively. At follow-up biopsy, 29 (21.6%) of participants (pooled placebo and pegozafermin) in the validation cohort had MASH resolution.
Table 1.
Baseline characteristics of participants with metabolic dysfunction-associated steatohepatitis in the derivation cohort
| Derivation cohort (N=95) | ||
|---|---|---|
| Demographic profile | ||
| Age (year) | 55.00 (44.50, 61.50) | |
| Female, n (%) | 64 (67.4) | |
| BMI (kg/m2) | 32.00 (30.00, 37.00) | |
| Diabetes mellitus, n (%) | 47 (49.5) | |
| Race, n (%) | ||
| White | 39 (41.1) | |
| Hispanic | 40 (42.1) | |
| African American | 2 (2.1) | |
| Asian | 13 (13.7) | |
| Native American | 1 (1.1) | |
| Interval between biopsies (years) | 1.33 (0.65, 2.62) | |
| Biochemical data | Baseline | At follow-up |
| AST (μ/L) | 38.00 (28.00, 60.00) | 34.00 (27.00, 58.00) |
| ALT (μ/L) | 48.00 (37.50, 79.00) | 43.00 (30.50, 68.50) |
| HbA1c (%) | 6.00 (6.00, 7.00) | 6.00 (6.00, 7.00) |
| Albumin (g/dL) | 4.00 (4.00, 5.00) | 4.00 (4.00, 5.00) |
| Total cholesterol (mg/dL) | 177.00 (151.00, 206.50) | 174.00 (151.00, 204.50) |
| HDL (mg/dL) | 43.00 (35.00, 52.00) | 47.00 (37.00, 56.50) |
| LDL (mg/dL) | 97.50 (81.00, 128.00) | 93.00 (74.50, 119.50) |
| TG (mg/dL) | 145.00 (111.00, 198.00) | 143.00 (110.00, 192.50) |
| NAS | 5.00 (4.00, 6.00) | 4.00 (3.00, 5.00) |
| Fibrosis stage 0 | 20 (21.1) | 26 (27.4) |
| Fibrosis stage 1 | 34 (35.8) | 26 (27.4) |
| Fibrosis stage 2 | 13 (13.7) | 12 (12.6) |
| Fibrosis stage 3 | 17 (17.9) | 18 (18.9) |
| Fibrosis stage 4 | 11 (11.6) | 13 (13.7) |
| Imaging results | ||
| MRI-PDFF (%) | 14.00 (9.50, 20.00) | 13.00 (8.00, 18.50) |
Median values are provided with IQR in parenthesis unless otherwise noted as n (%). ALT, alanine transaminase; AST, aspartate transaminase; BMI, body mass index; HbA1c, haemoglobin A1c; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MRI-PDFF, MRI-proton density fat fraction; NAS, nonalcoholic fatty liver disease activity score; TG, triglyceride.
Table 2.
Baseline characteristics of participants with metabolic dysfunction-associated steatohepatitis in the validation cohort
| Validation cohort (N=163) | ||
|---|---|---|
| Demographic profile | ||
| Age (year) | 56.0 (48.0, 62.0) | |
| Female, n (%) | 104 (63.8) | |
| BMI (kg/m2) | 36.50 (32.20, 40.20) | |
| Diabetes mellitus, n (%) | 104 (63.8) | |
| Race, n (%) | ||
| White | 152 (93.3) | |
| African American | 4 (2.5) | |
| Asian | 3 (1.8) | |
| Other | 4 (2.4) | |
| Biochemical data | At baseline | At follow-up |
| AST (μ/L) | 36.33 (27.67, 53.33) | 22.00 (17.00, 31.00) |
| ALT (μ/L) | 48.33 (35.33, 65.67) | 29.00 (21.00, 43.00) |
| HbA1c (%) | 6.50 (5.70, 7.40) | 6.10 (5.70, 7.00) |
| Albumin (g/dL) | 4.40 (4.20, 4.60) | 4.30 (4.10, 4.50) |
| Total cholesterol (mg/dL) | 168.00 (138.00, 205.00) | 167.00 (135.00, 199.00) |
| HDL (mg/dL) | 42.50 (35.50, 52.50) | 44.00 (37.00, 56.00) |
| LDL (mg/dL) | 94.25 (68.25, 117.00) | 91.00 (63.00, 115.00) |
| TG (mg/dL) | 157.00 (116.00, 200.50) | 137.00 (100.00, 177.00) |
| Histological data | ||
| NAS | 5.00 (4.00, 6.00) | 4.00 (3.00, 5.00) |
| Fibrosis stage 0 | – | 1 (0.6) |
| Fibrosis stage 1 | – | 15 (9.2) |
| Fibrosis stage 2 | 57 (35.0) | 51 (31.3) |
| Fibrosis stage 3 | 106 (65.0) | 83 (50.9) |
| Fibrosis stage 4 | – | 13 (8.0) |
| Imaging results | ||
| MRI-PDFF (%) | 16.00 (11.30, 22.10) | 8.20 (5.30, 14.60) |
Median values are provided with IQR in parenthesis unless otherwise noted as n (%).
ALT, alanine transaminase; AST, aspartate transaminase; BMI, body mass index; HbA1c, haemoglobin A1c; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MRI-PDFF, MRI-proton density fat fraction; NAS, nonalcoholic fatty liver disease activity score; TG, triglyceride.
Development of the MASH resolution index
Models that incorporated the baseline and percentage change in MRI-PDFF, the baseline and change in ALT, and a third variable, comprising one of baseline AST, baseline INR or baseline NAS were compared. Baseline AST was found to be the best variable to combine with a baseline and percentage change in MRI-PDFF, and baseline and a change in ALT, based on AIC, BIC and AUC (online supplemental table 1).
The MASH Resolution Index comprised baseline MRI-PDFF, percentage change in MRI-PDFF, baseline ALT, change in ALT, and baseline AST, and is represented by the following equation: 0.520–0.003×baseline ALT (U/L)–0.024×(latest ALT [U/L]- baseline ALT (U/L))–0.048×baseline MRI-PDFF–2.571×((latest MRI-PDFF-baseline MRI-PDFF)/baseline MRI-PDFF)–0.039×baseline AST (U/L). A calculator is available online (https://gastroenterology.ucsd.edu/research/masld/research/mash-resolution-index-calculator.html).
Diagnostic performance of the MASH Resolution Index in the derivation cohort
The AUC of the MASH Resolution Index for detecting MASH resolution in the derivation cohort was 0.81, 95% CI 0.69 to 0.93 (table 3) and had satisfactory calibration (online supplemental figure 1). The specificity, positive predictive value (PPV) and negative predictive value (NPV) at the 90% sensitivity cut-point were 33%, 24% and 93%, respectively (table 3). The sensitivity, PPV and NPV at the 90% specificity cut-point were 56%, 56% and 90%, respectively.
Table 3.
Diagnostic performance of the MASH Resolution Index (MASHResInd) to detect metabolic dysfunction-associated steatohepatitis resolution
| MASH resolution index | ||
|---|---|---|
| Derivation cohort | Validation cohort | |
| AUC | 0.81, 95% CI 0.69 to 0.93 | 0.83, 95% CI 0.76 to 0.91 |
| Cut-off | MASHResInd≤−2.67 | MASHResInd≤−2.67 |
| N | 27 | 17 |
| Sensitivity | 88.9% | 100.0% |
| Specificity | 32.5% | 12.7% |
| PPV | 23.5% | 19.9% |
| NPV | 92.6% | 100.0% |
| Grey zone, N (%) | 50 (52.6%) | 57 (35.0%) |
| Cut-off | MASHResInd≥−0.67 | MASHResInd≥−0.67 |
| N | 18 | 89 |
| Sensitivity | 55.6% | 89.7% |
| Specificity | 89.6% | 53.0% |
| PPV | 55.6% | 29.2% |
| NPV | 89.6% | 95.9% |
AUC, area under the receiver operating curve; MASH, metabolic dysfunction-associated steatohepatitis; NPV, negative predictive value; PPV, positive predictive value.
Diagnostic performance of the MASH Resolution Index in the validation cohort
The AUC of the MASH Resolution Index for identifying MASH resolution in the validation cohort was 0.83, 95% CI 0.76 to 0.91 (table 3) and calibrated well (online supplemental figure 2). The specificity, PPV and NPV at the 90% sensitivity cut-point were 13%, 20% and 100%, respectively (table 3). The sensitivity, PPV and NPV at the 90% specificity cut-point were 90%, 29% and 96%, respectively. The MASH Resolution Index had a positive likelihood ratio of 1.9 at the rule-in cut-point and a negative likelihood ratio of 0 at the rule-out cut-point in the validation cohort.
MASH resolution Index, compared with change in ALT and change in MRI-PDFF
The AUC (0.81, 95% CI 0.69 to 0.93) of the MASH Resolution Index was higher than the AUC of ALT response, MRI-PDFF response and combined ALT and MRI-PDFF response (figure 1A), absolute change in ALT, and percentage change in MRI-PDFF (figure 1B) for identifying MASH resolution in the derivation cohort.
Figure 1.
(A) The area under the receiver operating curve (AUC) of the MASH Resolution Index versus ALT response, MRI-PDFF response, and combined ALT and MRI-PDFF response for detecting MASH resolution in the derivation cohort. (B) The AUC of the MASH Resolution Index versus absolute change in ALT and percentage change in MRI-PDFF for detecting MASH resolution in the derivation cohort. ALT, alanine aminotransferase; MASH, metabolic dysfunction-associated steatohepatitis; MRI-PDFF, MRI-proton density fat fraction.
These findings remained consistent in the validation cohort, and the AUC of the MASH Resolution Index (0.83, 95% CI 0.76 to 0.91) outperformed that of ALT response, MRI-PDFF response, combined ALT and MRI-PDFF response (figure 2A), absolute change in ALT and percentage change in MRI-PDFF in the validation cohort (figure 2B).
Figure 2.
(A) The area under the receiver operating curve (AUC) of the MASH Resolution Index versus ALT response, MRI-PDFF response, and combined ALT and MRI-PDFF response for detecting MASH resolution in the validation cohort. (B) The AUC of the MASH Resolution Index versus absolute change in ALT and percentage change in MRI-PDFF for detecting MASH resolution in the validation cohort. ALT, alanine aminotransferase; MASH, metabolic dysfunction-associated steatohepatitis; MRI-PDFF, MRI-proton density fat fraction.
DISCUSSION
Main findings
Using two independent cohorts of well-characterised participants with longitudinal, contemporaneous liver biopsies and MRI-PDFF, we developed and externally validated the MASH Resolution Index, a non-invasive score to detect MASH resolution without worsening fibrosis. The MASH Resolution Index performed robustly in a distinct validation cohort with an AUC of 0.83. The MASH Resolution Index was designed to have two thresholds, with a positive likelihood ratio of 2 at the rule-in cut-point, and a negative likelihood ratio of 0 at the rule-out cut-point in the validation cohort. The MASH Resolution Index outperformed ALT response, MRI-PDFF-response, combined ALT and PDFF-response, and absolute changes in MRI-PDFF and ALT in identifying MASH resolution.
In context with current literature
Previous studies have evaluated the clinical utility of non-invasive tests to identify MASH resolution or histological response, with modest results. A meta-analysis of seven trials conducted in participants with MASH determined that ≥30% relative decline in MRI-PDFF was associated with higher odds of histological response.20 A secondary analysis of data from a clinical trial of obeticholic acid in participants with MASH determined that a ≥17 U/L decline in ALT was associated with histological response.22 A combination of MRI-PDFF response and ALT response was associated with a greater likelihood of histological response or MASH resolution compared with either MRI-PDFF or ALT response alone.24 However, the AUCs of these non-invasive tests for identifying MASH resolution were modest and did not exceed 0.66.24 The MASH Resolution Index provides superior accuracy to the previous non-invasive tests, with an AUC of 0.83 for identifying MASH resolution.
Strengths and limitations
The strengths of this study include validation in a prospective cohort, detailed clinical phenotyping, standardised evaluation and systematic exclusion of other causes of liver disease. However, this study is not without its limitations. The PPV of the high cut-point was more modest in the validation cohort but still outperformed available non-invasive tests, such as PDFF response (PPV 25.8%) and ALT response (PPV 23.7%). Furthermore, the PPV is dependent on the prevalence of MASH resolution and may change as more efficacious therapies emerge. The MASH Resolution Index can then be fine-tuned accordingly. The sample size and number of participants achieving MASH resolution were modest. Studies are required to determine if the MASH Resolution Index is associated with improvement in clinical outcomes such as reduced progression to cirrhosis or HCC, or whether it impacts the need for monitoring or surveillance.31 The MASH Resolution Index was derived and validated in cohorts in the USA and requires further validation in geographically distinct cohorts.
Implications for clinical practice and research
These data have important implications for clinical trial design and clinical practice. MASH resolution is a common endpoint in MASH clinical trials, however, liver biopsies are associated with uncommon, but potentially serious side effects. In addition, histology may be susceptible to misclassification due to sampling variability.13 Non-invasive assessment of MASH resolution represents a critical unmet need, and this study fills this important knowledge gap. In particular, the high NPV and low likelihood ratio of the rule-out cut-point are useful to identify individuals who are very unlikely to have developed MASH resolution, potentially sparing them from a liver biopsy. The MASH Resolution Index should be further validated in trials of therapeutics agents with different modes of action.
In summary, the MASH Resolution Index is a novel non-invasive score that may help detect MASH resolution. The MASH Resolution Index may be useful as an endpoint in phase 2A trials to increase the likelihood of a successful phase 2B trial. Further research is required to validate the use of the MASH Resolution Index in ethnically and geographically diverse cohorts.
Supplementary Material
Additional supplemental material is published online only. To view, please visit the journal online (https://doi.org/10.1136/gutjnl-2023-331401).
This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
WHAT IS ALREADY KNOWN ON THIS TOPIC
The resolution of metabolic dysfunction-associated steatohepatitis (MASH) is an accepted regulatory endpoint for subpart H approval of therapies developed for MASH but requires an invasive liver biopsy for its assessment. The performance of individual non-invasive tests to identify the resolution of MASH is modest.
WHAT THIS STUDY ADDS
Using two well-characterised, independent cohorts of adult participants with MASH who had a paired liver biopsy and contemporaneous MRI proton-density-fat fraction (PDFF) assessment, the authors developed the MASH Resolution Index, a non-invasive score comprising MRI-PDFF, alanine aminotransferase (ALT), and aspartate aminotransferase to detect MASH resolution, and externally validated it in a distinct cohort. The MASH Resolution Index performed robustly in a distinct validation cohort and outperformed changes in MRI-PDFF and ALT in identifying MASH resolution in both the derivation and validation cohorts.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
The MASH Resolution Index may be a useful score to non-invasively identify MASH resolution. This index may be used as an endpoint in phase 2A trials to increase the likelihood of a successful phase 2B trial.
Funding
RL receives funding support from NCATS (5UL1TR001442), NIDDK (U01DK061734, U01DK130190, R01DK106419, R01DK121378, R01DK124318, P30DK120515), NHLBI (P01HL147835) and John C Martin Foundation (RP124). DH receives funding support from the Singapore Ministry of Health’s National Medical Research Council (MOH-001370).
Competing 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, Madrigal, Metacrine, 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. DH has served as an advisory board member for Gilead. CBS reports grants from GE, Siemens, Philips, Bayer, Gilead, and Pfizer (grant is to UW-Madison; UCSD is a subcontract to UW-Madison); personal consultation fees from Blade, Boehringer and Epigenomics; consultation under the auspices of the University to AMRA, BMS, Exact Sciences, GE Digital, IBM-Watson, and Pfizer; lab service agreements from Enanta, Gilead, ICON, Intercept, Nusirt, Shire, Synageva, Takeda; royalties from Wolters Kluwer for educational material outside the submitted work; honoraria to the institution from Medscape for educational material outside the submitted work; executive position in Livivos (Chief Medical Officer) until 28 June 2023; current advisor to Livivos; ownership of stock options in Livivos; unpaid position in advisory board to Quantix Bio. MDG, SF and MM are employees of 89bio and own 89bio options or stocks.
Footnotes
Patient consent for publication Not applicable.
Ethics approval This study involves human participants and this study was approved by the Institutional Review Board at UC San Diego (IRB 111298). Participants gave informed consent to participate in the study before taking part.
Data availability statement
Data are not available.
REFERENCES
- 1.Loomba R, Friedman SL, Shulman GI. Mechanisms and disease consequences of nonalcoholic fatty liver disease. Cell 2021;184:2537–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Tan DJH, Ng CH, Lin SY, et al. Clinical characteristics, surveillance, treatment allocation, and outcomes of non-alcoholic fatty liver disease-related hepatocellular carcinoma: a systematic review and meta-analysis. Lancet Oncol 2022;23:521–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Huang DQ, Fowler KJ, Liau J, et al. Comparative efficacy of an optimal exam between ultrasound versus abbreviated MRI for HCC screening in NAFLD cirrhosis: a prospective study. Aliment Pharmacol Ther 2022;55:820–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Huang DQ, Singal AG, Kono Y, et al. Changing global epidemiology of liver cancer from 2010 to 2019: NASH is the fastest growing cause of liver cancer. Cell Metab 2022;34:969–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Tan DJH, Setiawan VW, Ng CH, et al. Global burden of liver cancer in males and females: changing etiological basis and the growing contribution of NASH. Hepatology 2023;77:1150–63. [DOI] [PubMed] [Google Scholar]
- 6.Huang DQ, Wilson LA, Behling C, et al. Fibrosis progression rate in biopsy-proven nonalcoholic fatty liver disease among people with diabetes versus people without diabetes: a multicenter study. Gastroenterology 2023;165:463–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Huang DQ, Noureddin N, Ajmera V, et al. Type 2 diabetes, hepatic decompensation, and hepatocellular carcinoma in patients with non-alcoholic fatty liver disease: an individual participant-level data meta-analysis. Lancet Gastroenterol Hepatol 2023;8:829–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.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;75:284–91. [DOI] [PubMed] [Google Scholar]
- 9.Younossi ZM, Koenig AB, Abdelatif D, et al. Global epidemiology of nonalcoholic fatty liver disease-meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 2016;64:73–84. [DOI] [PubMed] [Google Scholar]
- 10.Estes C, Razavi H, Loomba R, et al. Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease. Hepatology 2018;67:123–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Loomba R, Ratziu V, Harrison SA, et al. Expert panel review to compare FDA and EMA guidance on drug development and endpoints in nonalcoholic steatohepatitis. Gastroenterology 2022;162:680–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Harrison SA, Allen AM, Dubourg J, et al. Challenges and opportunities in NASH drug development. Nat Med 2023;29:562–73. [DOI] [PubMed] [Google Scholar]
- 13.Ratziu V, Charlotte F, Heurtier A, et al. Sampling variability of liver biopsy in Nonalcoholic fatty liver disease. Gastroenterology 2005;128:1898–906. [DOI] [PubMed] [Google Scholar]
- 14.Davison BA, Harrison SA, Cotter G, et al. Suboptimal reliability of liver biopsy evaluation has implications for randomized clinical trials. J Hepatol 2020;73:1322–32. [DOI] [PubMed] [Google Scholar]
- 15.Caussy C, Reeder SB, Sirlin CB, et al. Quantitative assessment of liver fat by MRI-PDFF as an endpoint in NASH trials. Hepatology 2018;68:763–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Middleton MS, Heba ER, Hooker CA, et al. Agreement between magnetic resonance imaging proton density fat fraction measurements and pathologist-assigned steatosis grades of liver biopsies from adults with nonalcoholic steatohepatitis. Gastroenterology 2017;153:753–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Yokoo T, Serai SD, Pirasteh A, et al. Linearity, bias, and precision of hepatic proton density fat fraction measurements by using MR imaging: a meta-analysis. Radiology 2018;286:486–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Starekova J, Reeder SB. Liver fat quantification: where do we stand Abdom Radiol (NY) 2020;45:3386–99. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Noureddin M, Lam J, Peterson MR, et al. Utility of magnetic resonance imaging versus histology for quantifying changes in liver fat in nonalcoholic fatty liver disease trials. Hepatology 2013;58:1930–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Stine JG, Munaganuru N, Barnard A, et al. Change in MRI-PDFF and histologic response in patients with nonalcoholic steatohepatitis: a systematic review and meta-analysis. Clin Gastroenterol Hepatol 2021;19:2274–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Loomba R, Neuschwander-Tetri BA, Sanyal A, et al. Multicenter validation of association between decline in MRI-PDFF and histologic response in NASH. Hepatology 2020;72:1219–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Loomba R, Sanyal AJ, Kowdley KV, et al. Factors associated with histologic response in adult patients with nonalcoholic steatohepatitis. Gastroenterology 2019;156:88–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Tamaki N, Munaganuru N, Jung J, et al. Clinical utility of 30% relative decline in MRI-PDFF in predicting fibrosis regression in non-alcoholic fatty liver disease. Gut 2022;71:983–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Huang DQ, Sharpton SR, Amangurbanova M, et al. Clinical utility of combined MRI-PDFF and ALT response in predicting histologic response in nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol 2023;21:2682–5. [DOI] [PubMed] [Google Scholar]
- 25.Loomba R, Sanyal AJ, Kowdley KV, et al. Randomized, controlled trial of the FGF21 analogue pegozafermin in NASH. N Engl J Med 2023;389:998–1008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Kleiner DE, Brunt EM, Van Natta M, et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 2005;41:1313–21. [DOI] [PubMed] [Google Scholar]
- 27.Tang A, Desai A, Hamilton G, et al. Accuracy of MR imaging-estimated proton density fat fraction for classification of dichotomized histologic steatosis grades in nonalcoholic fatty liver disease. Radiology 2015;274:416–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Tang A, Tan J, Sun M, et al. Nonalcoholic fatty liver disease: MR imaging of liver proton density fat fraction to assess hepatic steatosis. Radiology 2013;267:422–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Caussy C, Soni M, Cui J, et al. Nonalcoholic fatty liver disease with cirrhosis increases familial risk for advanced fibrosis. J Clin Invest 2017;127:2697–704. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Tincopa MA, Loomba R. Non-invasive diagnosis and monitoring of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis. Lancet Gastroenterol Hepatol 2023;8:660–70. [DOI] [PubMed] [Google Scholar]
- 31.Huang DQ, Singal AG, Kanwal F, et al. Hepatocellular carcinoma surveillance - utilization, barriers and the impact of changing aetiology. Nat Rev Gastroenterol Hepatol 2023;20:797–809. [DOI] [PubMed] [Google Scholar]
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