Metabolic dysfunction-associated steatotic liver disease (MASLD) exhibits considerable variability in clinical outcomes. Identifying specific phenotypic profiles within MASLD is essential for developing targeted therapeutic strategies. Here we investigated the heterogeneity of MASLD using partitioning around medoids clustering based on six simple clinical variables in a cohort of 1,389 individuals living with obesity. The identified clusters were applied across three independent MASLD cohorts with liver biopsy (totaling 1,099 participants), and in the UK Biobank to assess the incidence of chronic liver disease, cardiovascular disease and type 2 diabetes. Results unveiled two distinct types of MASLD associated with steatohepatitis on histology and liver imaging. The first cluster, liver-specific, was genetically linked and showed rapid progression of chronic liver disease but limited risk of cardiovascular disease. The second cluster, cardiometabolic, was primarily associated with dysglycemia and high levels of triglycerides, leading to a similar incidence of chronic liver disease but a higher risk of cardiovascular disease and type 2 diabetes. Analyses of samples from 831 individuals with available liver transcriptomics and 1,322 with available plasma metabolomics highlighted that these two types of MASLD exhibited distinct liver transcriptomic profiles and plasma metabolomic signatures, respectively. In conclusion, these data provide preliminary evidence of the existence of two distinct types of clinically relevant MASLD with similar liver phenotypes at baseline, but each with specific underlying biological profiles and different clinical trajectories, suggesting the need for tailored therapeutic strategies.

There is substantial inter-individual variability in the risk of non-alcoholic fatty liver disease (NAFLD). Part of which is explained by insulin resistance (IR) (‘MetComp’) and part by common modifiers of genetic risk (‘GenComp’). We examined how IR on the one hand and genetic risk on the other contribute to the pathogenesis of NAFLD. We studied 846 individuals: 492 were obese patients with liver histology and 354 were individuals who underwent intrahepatic triglyceride measurement by proton magnetic resonance spectroscopy. A genetic risk score was calculated using the number of risk alleles in PNPLA3, TM6SF2, MBOAT7, HSD17B13 and MARC1. Substrate concentrations were assessed by serum NMR metabolomics. In subsets of participants, non-esterified fatty acids (NEFAs) and their flux were assessed by D₅-glycerol and hyperinsulinemic-euglycemic clamp (n = 41), and hepatic de novo lipogenesis (DNL) was measured by D₂O (n = 61). We found that substrate surplus (increased concentrations of 28 serum metabolites including glucose, glycolytic intermediates, and amino acids; increased NEFAs and their flux; increased DNL) characterized the ‘MetComp’. In contrast, the ‘GenComp’ was not accompanied by any substrate excess but was characterized by an increased hepatic mitochondrial redox state, as determined by serum β-hydroxybutyrate/acetoacetate ratio, and inhibition of hepatic pathways dependent on tricarboxylic acid cycle activity, such as DNL. Serum β-hydroxybutyrate/acetoacetate ratio correlated strongly with all histological features of NAFLD. IR and hepatic mitochondrial redox state conferred additive increases in histological features of NAFLD. These data show that the mechanisms underlying ‘Metabolic’ and ‘Genetic’ components of NAFLD are fundamentally different. These findings may have implications with respect to the diagnosis and treatment of NAFLD. The pathogenesis of non-alcoholic fatty liver disease can be explained in part by a metabolic component, including obesity, and in part by a genetic component. Herein, we demonstrate that the mechanisms underlying these components are fundamentally different: the metabolic component is characterized by hepatic oversupply of substrates, such as sugars, lipids and amino acids. In contrast, the genetic component is characterized by impaired hepatic mitochondrial function, making the liver less able to metabolize these substrates.

Context Guidelines recommend blood-based fibrosis biomarkers to identify advanced nonalcoholic fatty liver disease (NAFLD), which is particularly prevalent in patients with obesity. Objective To study whether the degree of obesity affects the performance of liver fibrosis biomarkers in NAFLD. Design Cross-sectional cohort study comparing simple fibrosis scores [Fibrosis-4 Index (FIB-4); NAFLD Fibrosis Score (NFS); aspartate aminotransferase to platelet ratio index; BARD (body mass index, aspartate-to-alanine aminotransferase ratio, diabetes); Hepamet Fibrosis Score (HFS)] and newer scores incorporating neo-epitope biomarkers PRO-C3 (ADAPT, FIBC3) or cytokeratin 18 (MACK-3). Setting Tertiary referral center. Patients We recruited overweight/obese patients from endocrinology (n = 307) and hepatology (n = 71) clinics undergoing a liver biopsy [median body mass index (BMI) 40.3 (interquartile range 36.0-44.7) kg/m²]. Additionally, we studied 859 less obese patients with biopsy-proven NAFLD to derive BMI-adjusted cutoffs for NFS. Main Outcome Measures Biomarker area under the receiver operating characteristic (AUROC), sensitivity, specificity, and predictive values to identify histological stage ≥F3 fibrosis or nonalcoholic steatohepatitis with ≥F2 fibrosis [fibrotic nonalcoholic steatohepatitis (NASH)]. Results The scores with an AUROC ≥0.85 to identify ≥F3 fibrosis were ADAPT, FIB-4, FIBC3, and HFS. For fibrotic NASH, the best predictors were MACK-3 and ADAPT. The specificities of NFS, BARD, and FIBC3 deteriorated as a function of BMI. We derived and validated new cutoffs for NFS to rule in/out ≥F3 fibrosis in groups with BMIs <30.0, 30.0 to 39.9, and ≥40.0 kg/m². This optimized its performance at all levels of BMI. Sequentially combining FIB-4 with ADAPT or FIBC3 increased specificity to diagnose ≥F3 fibrosis. Conclusions In obese patients, the best-performing fibrosis biomarkers are ADAPT and the inexpensive FIB-4, which are unaffected by BMI. The widely used NFS loses specificity in obese individuals, which may be corrected with BMI-adjusted cutoffs.

Pathologists quantify liver steatosis as the fraction of lipid droplet-containing hepatocytes out of all hepatocytes, whereas the magnetic resonance-determined proton density fat fraction (PDFF) reflects the tissue triacylglycerol concentration. We investigated the linearity, agreement, and correspondence thresholds between histological steatosis and PDFF across the full clinical spectrum of liver fat content associated with non-alcoholic fatty liver disease. Using individual patient-level measurements, we conducted a systematic review and meta-analysis of studies comparing histological steatosis with PDFF determined by magnetic resonance spectroscopy or imaging in adults with suspected non-alcoholic fatty liver disease. Linearity was assessed by meta-analysis of correlation coefficients and by linear mixed modelling of pooled data, agreement by Bland–Altman analysis, and thresholds by receiver operating characteristic analysis. To explain observed differences between the methods, we used RNA-seq to determine the fraction of hepatocytes in human liver biopsies. Eligible studies numbered 9 (N = 597). The relationship between PDFF and histology was predominantly linear (r = 0.85 [95% CI, 0.80–0.89]), and their values approximately coincided at 5% steatosis. Above 5% and towards higher levels of steatosis, absolute values of the methods diverged markedly, with histology exceeding PDFF by up to 3.4-fold. On average, 100% histological steatosis corresponded to a PDFF of 33.0% (29.5–36.7%). Targeting at a specificity of 90%, optimal PDFF thresholds to predict histological steatosis grades were ≥5.75% for ≥S1, ≥15.50% for ≥S2, and ≥21.35% for S3. Hepatocytes comprised 58 ± 5% of liver cells, which may partly explain the lower values of PDFF vs. histology. Histological steatosis and PDFF have non-perfect linearity and fundamentally different scales of measurement. Liver fat values obtained using these methods may be rendered comparable by conversion equations or threshold values. Magnetic resonance-proton density fat fraction (PDFF) is increasingly being used to measure liver fat in place of the invasive liver biopsy. Understanding the relationship between PDFF and histological steatosis fraction is important for preventing misjudgement of clinical status or treatment effects in patient care. Our analysis revealed that histological steatosis fraction is often significantly higher than PDFF, and their association varies across the spectrum of fatty liver severity. These findings are particularly important for physicians and clinical researchers, who may use these data to interpret PDFF measurements in the context of histologically evaluated liver fat content.

Metabolic dysfunction-associated steatohepatitis (MASH) is associated with a >10-fold increase in liver-related mortality. However, biomarkers predicting both MASH and mortality in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) are missing. We developed a metabolome-derived prediction score for MASH and examined whether it predicts mortality in Chinese and European cohorts. The MASH prediction score was developed using a multi-step machine learning strategy, based on 44 clinical parameters and 250 serum metabolites measured by proton nuclear magnetic resonance in 311 Chinese adults undergoing a liver biopsy. External validation was conducted in a Finnish liver biopsy cohort (n = 305). We investigated associations of the score with all-cause and cause-specific mortality in the population-based Shanghai Changfeng study (n = 5,893) and the UK biobank (n = 111,673). A total of 24 clinical parameters and 194 serum metabolites were significantly associated with MASH in the Chinese liver biopsy cohort. The final MASH score included BMI, aspartate aminotransferase, tyrosine, and the phospholipid-to-total lipid ratio in VLDL. The score identified patients with MASH with AUROCs of 0.87 (95% CI 0.83-0.91) and 0.81 (95% CI 0.75-0.88) in the Chinese and Finnish cohorts, with high negative predictive values. Participants with a high or intermediate risk of MASH based on the score had a markedly higher risk of MASLD-related mortality than those with a low risk in Chinese (hazard ratio 23.19; 95% CI 4.80-111.97) and European (hazard ratio 20.15; 95% CI 10.95-37.11) individuals after 7.2 and 12.6 years of follow-up, respectively. The MASH prediction score was superior to the Fibrosis-4 index and the NAFLD fibrosis score in predicting MASLD-related mortality. The metabolome-derived MASH prediction score accurately predicts risk of MASH and MASLD-related mortality in both Chinese and European individuals. Metabolic dysfunction-associated steatohepatitis (MASH) is associated with more than a 10-fold increase in liver-related death. However, biomarkers predicting not only MASH, but also death due to liver disease, are missing. We established a MASH prediction score based on 44 clinical parameters and 250 serum metabolites using a machine learning strategy. This metabolome-derived MASH prediction score could accurately identify patients with MASH among both Chinese and Finnish individuals, and it was superior to the Fibrosis-4 index and the NAFLD fibrosis score in predicting MASLD-related death in the general population. Thus, the new MASH prediction score is a useful tool for identifying individuals with a markedly increased risk of serious liver-related outcomes among at-risk and general populations.

Fatty liver plays a pivotal role in the pathogenesis of the metabolic syndrome and type 2 diabetes. According to an updated classification, any individual with liver steatosis and one or more features of the metabolic syndrome, without excess alcohol consumption or other known causes of steatosis, has metabolic dysfunction-associated steatotic liver disease (MASLD). Up to 60–70% of all individuals with type 2 diabetes have MASLD. However, the prevalence of advanced liver fibrosis in type 2 diabetes remains uncertain, with reported estimates of 10–20% relying on imaging tests and likely overestimating the true prevalence. All stages of MASLD impact prognosis but fibrosis is the best predictor of all-cause and liver-related mortality risk. People with type 2 diabetes face a two- to threefold increase in the risk of liver-related death and hepatocellular carcinoma, with 1.3% progressing to severe liver disease over 7.7 years. Because reliable methods for detecting steatosis are lacking, MASLD mostly remains an incidental finding on imaging. Regardless, several medical societies advocate for universal screening of individuals with type 2 diabetes for advanced fibrosis. Proposed screening pathways involve annual calculation of the Fibrosis-4 (FIB-4) index, followed by a secondary test such as transient elastography (TE) for intermediate-to-high-risk individuals. However, owing to unsatisfactory biomarker specificity, these pathways are expected to channel approximately 40% of all individuals with type 2 diabetes to TE and 20% to tertiary care, with a false discovery rate of up to 80%, raising concerns about feasibility. There is thus an urgent need to develop more effective strategies for surveying the liver in type 2 diabetes. Nonetheless, weight loss through lifestyle changes, pharmacotherapy or bariatric surgery remains the cornerstone of management, proving highly effective not only for metabolic comorbidities but also for MASLD. Emerging evidence suggests that fibrosis biomarkers may serve as tools for risk-based targeting of weight-loss interventions and potentially for monitoring response to therapy. Graphical Abstract