Data-driven cluster analysis identifies distinct types of metabolic dysfunction-associated steatotic liver disease

Nonalcoholic fatty liver disease, now referred to as metabolic dysfunction-associated steatotic liver disease (MASLD) 1 , 2 , is currently the most common chronic liver disease worldwide, with an estimated global prevalence of approximately 30% (ref. 3 ). MASLD comprises a spectrum of disorders ranging from isolated steatosis to metabolic dysfunction-associated steatohepatitis (MASH), ultimately leading to advanced fibrosis, cirrhosis and hepatocellular carcinoma 4 . However, not every individual diagnosed with MASLD will progress to MASH and later stages of liver disease, indicating the presence of a substantial interindividual variation in the disease progression 5 . Furthermore, MASLD harbors an increased risk of cardiovascular disease and type 2 diabetes 6 , 7 , which also widely varies among individuals. This interindividual variability in the severity and progression of MASLD and its extrahepatic consequences, together with the challenges of finding a specific drug treatment, highlight the need for more personalized approaches 8 – 10 . Given this context, advancements in diagnostic strategies for risk stratification and efficient testing of new drugs in at-risk populations are urgently needed 11 . Emerging evidence points to the clinical relevance of distinguishing different types of MASLD on the basis of distinct pathophysiological mechanisms and rates of disease progression 5 . For example, genetic predisposition to hepatic steatosis is associated with increased risk of liver-related events, while offering protection against coronary artery disease 12 , 13 . Specifically, PNPLA3 rs738409 (p.I148M), the strongest genetic variant predisposing to MASLD, is associated with a reduction in intrahepatic turnover of lipids droplets but is not causally linked to ischemic heart disease in individuals with MASLD 14 . In contrast, other mechanisms central to MASLD pathophysiology, such as hepatic de novo lipogenesis or adipose tissue dysfunction, have been associated with insulin resistance and a higher risk for type 2 diabetes and cardiovascular disease, but with only a moderate risk of liver-related events 10 . In the present study, we identified two types of MASLD by using a data-driven clustering approach focused on key hepatic and cardiometabolic traits. These two MASLD types have distinct biological profiles and risks for cardiometabolic disease and diabetes, despite having the same severity of MASLD on liver histology. We then clustered four independent cohorts of individuals at-risk for MASLD from Italy, Finland, Belgium and the United Kingdom, with consistent results, supporting the validity of the proposed clustering.

MASLD has a strong genetic component with variants in PNPLA3 , TM6SF2 , MBOAT7 and GCKR accounting for a large fraction of its heritability and accelerating liver disease progression to MASH, cirrhosis and hepatocellular carcinoma 15 – 17 . We hypothesized that the liver-specific cluster could be enriched in these genetic variants. Therefore, we examined the difference of polygenic risk score of hepatic fat content (PRS-HFC) distribution in the liver-specific cluster 5 compared with the cardiometabolic and control clusters in ABOS, finding an enrichment of PRS-HFC in this cluster (adjusted P = 0.034 and adjusted P < 0.001 versus the cardiometabolic and control clusters, respectively) (Table 1 ). Results were similar when we considered only the PNPLA3 rs738409 variant ( P < 0.01 and P < 0.001 versus the cardiometabolic and control clusters, respectively) (Fig. 3 ). These results were confirmed in UK Biobank participants (Extended Data Table 2 ). Fig. 3 Genotype distribution of the PNPLA3 rs738409 C > G stratified by clusters in the ABOS cohort and UK Biobank and differential hepatic gene expression and plasma metabolomics across clusters in the ABOS cohort. a , Genotype distribution of the PNPLA3 rs738409 C > G stratified by clusters in the ABOS cohort. The bar graph shows the percentages of homozygotes (GG) and heterozygotes (CG) patients at risk across liver-specific (LS), cardiometabolic (CM) and control clusters. Statistical tests were chi-squared test or Fisher exact test as appropriate, two-sided with Bonferroni correction. Significance levels are indicated as follows: $ indicates P = 0.0079, *** P < 0.001. b , c , Differential hepatic gene expression and plasma metabolomics across clusters. The Euler diagrams illustrate the differential gene expression in liver tissue ( b ) and plasma metabolomics ( c ), across the three clusters: cardiometabolic (CM), liver-specific (LS) and control (CTRL). The sizes of the areas in the Euler diagram are proportional to the number of differentially expressed features they represent.

We then explored the added value of the proposed clustering, beyond each of its individual components, to predict the various clinical outcomes. For that purpose, for each outcome, we first examined the overall predictive power of each variable of interest compared with clustering alone. No individual variable performed better than clustering at predicting simultaneously the three clinical outcomes (Extended Data Table 4 ). For example, ALT alone predicted incident chronic liver disease better than clustering, but clustering was superior at predicting cardiovascular disease. In contrast, HbA1c predicted incident cardiovascular disease better than clustering, but clustering performed better in the prediction of chronic liver disease. Likewise, among patients without diabetes at the time of inclusion, age, BMI, HbA1c, ALT and triglycerides performed better in predicting the risk of incident diabetes better than clustering alone. In contrast, clustering did better than LDL cholesterol alone at predicting all outcomes. Second, we performed multivariable analyses, in which the clustering model was first adjusted for sex, age and alcohol use, and second, one by one, ALT, HbA1c, triglycerides, BMI or LDL cholesterol (Fig. 5 ). Although in most cases the HR estimates of at-risk clusters were reduced after further adjustment for one other clustering variable, all values remained statistically significant compared with the control cluster in at least one at-risk cluster for each outcome. Collectively, these data show that clustering was superior to each individual variable in predicting simultaneously all three clinical trajectories. Fig. 5 Added value of the clustering model to predict cumulative incidence of chronic liver disease, cardiovascular disease and type 2 diabetes, among UK Biobank participants. a – c , Added value of the clustering model to predict cumulative incidence of chronic liver disease ( a ), cardiovascular disease ( b ) and type 2 diabetes ( c ), among UK Biobank participants. Multivariable analyses evaluating the predictive value of the clustering model adjusted for age/sex/alcohol, independently of each additional individual variable, are included in the clustering. TG, triglycerides. The dots represent the HR estimates, and the error bars represent the 95% CIs.

To further elucidate biological differences between the cardiometabolic and liver-specific clusters, we analyzed the metabolomics data available in ABOS (Fig. 3 ). When comparing the cardiometabolic and liver-specific clusters, we observed increased concentrations of carbohydrates in the cardiometabolic cluster (Extended Data Fig. 3 ), reflecting the dysglycemic state (Table 1 ). However, most differences concerned amino acid and lipid metabolites, and particularly the amino acid metabolites tyramine O -sulfate, homocitrulline, p -cresol glucuronide, phenylacetylglutamine, phenylacetylglutamate, 4-hydroxyphenylacetylglutamine, 4-hydroxyphenylacetate and imidazole propionate, previously associated with the gut microbiota 20 – 22 , had the highest and most significant increase in the cardiometabolic cluster. Deoxycholate, a secondary bile acid, was also elevated, suggesting changes in lipid metabolism and liver function. These metabolites were also differentially abundant between the cardiometabolic and control clusters (Extended Data Fig. 3 and Supplementary Table 1 ) and, therefore, probably linked to the dysmetabolic state. Differences were also observed in the comparison between the liver-specific and control clusters, with elevated levels of 5α-androstan-3α,17β-diol monosulfate, its disulfate form, glycoursodeoxycholic acid sulfate, and taurochenodeoxycholic acid 3-sulfate suggesting changes in steroid processing. Furthermore, higher levels of ursodeoxycholate, glycochenodeoxycholate glucuronide and glycochenodeoxycholate 3-sulfate and decreased levels of cysteine-glutathione disulfide were observed in both the liver-specific and cardiometabolic clusters compared with the control cluster (Extended Data Fig. 3 and Supplementary Table 1 ). Possibly linked to oxidative stress and liver function, we observed decreased levels of cysteine-glutathione disulfide both in the liver-specific and in the cardiometabolic cluster compared with the control cluster, thus indicating that reduced antioxidant capacity might be a common feature in the two MASH subtypes or a consequence of the severe phenotype. Taken together, these transcriptomics and metabolomics analyses support the existence of two biologically distinct types of severe MASLD.

In the present study, using unsupervised hard clustering, we identified two distinct endotypes of at-risk MASLD, namely, cardiometabolic MASLD and liver-specific MASLD. Both types were characterized by a severe liver phenotype at baseline; however, they showed different underlying biological profiles and distinct clinical progression patterns. These two newly defined types of MASLD could be robustly identified in several independent and well-characterized cohorts, using a simple algorithm based on six widely available traits: age, BMI, HbA1c, ALT, LDL cholesterol and triglycerides ( https://ulr-metrics.univ-lille.fr/masldclusters/ ). The two types of at-risk MASLD could not be distinguished by their liver phenotype assessed by histology nor by MRI, and they were both associated with an increased risk of incident chronic liver disease. The cardiometabolic MASLD was, however, specifically characterized by a higher prevalence of dyslipidemia, hypertension and dysglycemia, resulting in a high risk of incident cardiovascular disease and type 2 diabetes. In contrast, the liver-specific MASLD was characterized by a more pronounced elevation of liver enzymes at a younger age and showed limited risk of diabetes progression and incident cardiovascular disease. The liver-specific MASLD was also characterized by a specific genetic background with a higher frequency of the minor allele of PNPLA3 rs738409 and a higher polygenic risk score for hepatic fat content. Importantly, the proposed clustering outperformed its individual components in simultaneously predicting liver phenotype and future risk of the different clinical outcomes. As expected, several individual continuous variables also showed a good predictive value for predicting specific clinical outcomes in the overall UK Biobank population, namely, ALT for chronic liver disease and HbA1c for cardiovascular disease and incident diabetes. In contrast, the clustering approach surpassed all individual variables for simultaneously predicting the three outcomes. Of note, after adjustment for ALT in multivariable analysis, the risk of chronic liver disease became lower in the liver-specific cluster than in the control cluster, while it remained increased in the cardiometabolic cluster. Confirming the strong association between the risk of liver disease and ALT in the liver-specific cluster, this result also indicates that ALT may overestimate the risk of chronic liver disease when other clustering variables are not considered. Similarly, the positive association between the cardiometabolic cluster and cardiovascular risk became negative after adjustment for HbA1c, suggesting that HbA1c alone may overestimate the risk of cardiovascular disease, in which other clustering variables such as triglycerides or age may favor cardiovascular disease, independently of dysglycemia. Finally, in the liver-specific cluster, the elevated risk of incident diabetes was eliminated after adjustment for ALT, underlying the specific role played by the liver in the physiopathology of dysglycemia 23 . Taken together, our findings highlight the potential of clustering to provide a more comprehensive risk assessment, identifying patients at risk for a range of liver and cardiometabolic diseases rather than focusing on a single condition. In addition, the resulting assignment of individuals into two clearly labeled clusters of at risk MASLD facilitated the exploration of their biological nature. Specifically, the cardiometabolic cluster exhibited unique liver gene transcripts and pathways not present in type 2 diabetes, involving lipid transport, immune response and inflammation and vascular function-related pathways. In addition, metabolomic analyses identified numerous metabolites common to both type 2 diabetes and the cardiometabolic cluster, mostly linked to dysglycemia but also some metabolites uniquely associated with the cardiometabolic cluster. These unique metabolites, including glycerophospholipids, sphingolipids and bile acid metabolites, indicate specific disturbances in lipid processing, protein and energy metabolism, and inflammation. The cardiometabolic cluster was also characterized by an increase of several gut microbiota metabolites previously linked to insulin resistance and diabetes pathogenesis, such as imidazole propionate, p -cresol glucuronide, phenylacetylglutamine, 4-hydroxyphenylacetylglutamine and phenylacetylglutamate 20 – 22 . Similarly, higher levels of p -cresol glucuronide and 4-hydroxyphenylacetylglutamine have been linked to cardiovascular toxicity and mortality 22 , 24 , 25 . These metabolites, which are produced by the gut microbiota from aromatic amino acids, might explain at least in part the increased cardiovascular risk observed in this cluster. In contrast, the liver-specific MASLD was more related to changes in lipid metabolism confined to the hepatocyte, in line with its specific genetic background. In this study we identify distinctive endotypes of at-

The UZA cohort included 467 patients referred to the Obesity Clinic at Antwerp University Hospital, Edegem, Belgium, for suspected MASLD based on imaging and biochemistry data. The collection of clinical, anthropometric and histological data has been previously described 47 , 48 . A percutaneous or laparoscopic-guided percutaneous liver needle biopsy was performed on participants with overweight/obesity as part of the Hepatic and Adipose Tissue and Functions in Metabolic Syndrome (HEPADIP) study (Belgian registration number B30020071389, Antwerp University Hospital File 6/25/125) as previously described 47 . Liver histology was assessed according to the NASH CRN 45 , 46 . Individuals with alcohol consumption above 30/20 g per day in men/women were excluded from the analysis. Written informed consent was obtained from all patients in both cohorts, and the studies were conducted in conformity with the Declaration of Helsinki.

The Helsinki cohort enrolled 343 consecutive individuals with morbid obesity eligible for bariatric surgery and 42 consecutive individuals with a BMI ≥25 kg m −2 undergoing liver biopsy for suspected MASH, all recruited between 2006 and 2018 at the Helsinki University Hospital, Helsinki, Finland. A week before the liver biopsy, participants underwent clinical examination and blood sampling as previously described 50 . Liver histology was assessed according to the NASH CRN 45 , 46 , as described above. Individuals with alcohol consumption above 30/20 g per day in men/women were excluded from the analysis. The study was approved by the Local Research Ethics Committee at Helsinki University Hospital. All participants gave written informed consent to the study.

Six variables associated with MASLD physiopathology and increased risk of MASH were selected for clustering in ABOS, namely, age, BMI, HbA1c, ALT, LDL cholesterol and circulating triglycerides. Cluster analysis and identification of MASLD subtypes were performed on 1,389 ABOS participants (Fig. 1 ), after the exclusion of 54 patients for self-declaration alcohol consumption above 50/60 g per day for women and men, respectively, at the first visit, to avoid any risk of inclusion of patients with alcohol-related liver disease; 58 participants for a BMI ≤30 kg m −2 ; 27 participants for missing values in clustering traits (that is, age, BMI, HbA1c, ALT, LDL cholesterol and circulating triglycerides); and 17 participants having absolute standardized values of 5 or higher in at least one of the clustering traits (Extended Data Fig. 1 ). The analysis was performed using the partitioning around medoids method in R (package ‘cluster’, version 2.1.4) 57 , which is a more robust version of k -means clustering. Distances were computed as Euclidean distances using standardized variables scaled to a mean of 0 and a standard deviation of 1. To estimate the optimal number of clusters, we evaluated the silhouette widths 58 for each clustering, varying the number of clusters going from three clusters to ten clusters. We determined the optimal number of clusters by choosing the configuration that yielded the highest silhouette coefficients, signifying well-delineated clusters whose members are closely related to one another and distinctly separate from individuals in other clusters. We then assessed the stability of the resulting clusters using the R function clusterboot from the fpc package (v.2.2-12), by resampling 2,000 times the original data and computing the Jaccard similarities of the original clusters to the most similar clusters in the resampled data. The mean (standard deviation) Jaccard-similarity measure was 0.73 (0.07) across all clusters. Data from the UZA, MAFALDA and Helsinki cohorts were normalized using ABOS values for centering and scaling. Then, participants were allocated to the cluster they were most similar to after the exclusion of participants having absolute standardized values of 5 or higher in at least one of the clustering traits, calculated as their Euclidean distance from the nearest cluster medoid derived from ABOS coordinates. Data from the UK biobank cohorts were normalized using ABOS values for centering and scaling. Participants were allocated to the cluster they were most similar to after the exclusion of those with self-reported history or medical diagnosis of other causes of liver disease, with a medical diagnosis of the target longitudinal outcome at baseline, or having absolute standardized values of 5 or higher in at least one of the clustering traits, calculated as their Euclidean distance from the nearest cluster medoid derived from ABOS coordinates. The Calinski–Harabasz Index was 263 for the ABOS cohort and reached 174 in the validation cohort, indicating well-defined clusters and confirming the transportability of the proposed stratification in diverse populations. In the UK Biobank cohort, encompassing a broader BMI range and less clinically extreme cases, the Calinski–Harabasz Index increases even further to 18,774, probably due to the larger and more diverse sample size.

In the ABOS cohort, genotyping was available for 1,259 participants and was performed using the Illumina Infinium assay 59 . This analysis was conducted at the SNO&SEQ Technology Platform, Molecular Medicine, BMC, Husargatan 3, Uppsala, Sweden. Results were analyzed using the software GenomeStudio 2.0.3. The following variants were assessed: PNPLA3 rs738409 C > G (p.I148M), TM6SF2 rs58542926 C > T (p.E167K), MBOAT7 rs641738 C > T and GCKR rs1260326 C > T (p.P446L). In the UK Biobank, genotyping was available for approximately 490,000 individuals and was performed using two similar genotyping arrays (that is Affymetrix UK BiLEVE and UK Biobank Axiom arrays) as described elsewhere 60 . The following variants were assessed: PNPLA3 rs738409 C > G (p.I148M), TM6SF2 rs58542926 C > T (p.E167K), MBOAT7 rs641738 C > T and GCKR rs1260326 C > T (p.P446L). The PRS-HFC was computed according to the originally reported formula 61 .

Liver transcriptomic data were available for a subset of 831 participants from the ABOS cohort, as previously described 62 . Total RNA was extracted from 30 mg frozen liver biopsies for Affymetrix microarray analysis using TRIzol reagent (Thermo Fisher Scientific), followed by purification on RNeasy columns (Qiagen). RNA purity and quantity were assessed using a Nanodrop spectrometer (Thermo Fisher Scientific). RNA integrity was quantified using the Agilent RNA6000 Nano assay and an Agilent 2100 BioAnalyzer. Raw data from Affymetrix microarrays were first processed with robust multi-array average (RMA) with GC correction and scale intensities (CG-RMA-scale) as a normalization method.

Data were reported as median (interquartile range) for continuous variables and frequencies (percentages) for categorical variables. Clusters were compared using the Kruskal–Wallis test, chi-squared test or Fisher’s exact test, as appropriate. Raw P values were adjusted for multiple testing separately for clinical data, histological data and genetic data. To control the family-wise error rate, the Bonferroni method was used. Differences were considered statistically significant when adjusted P value(s) were less than 0.05. For statistically significant variables, post hoc analysis was performed comparing pairwise MASH-enriched MASLD clusters (2 and 5) and the combined nonenriched MASLD clusters (1, 3, 4 and 6) using the Dunn test, chi-squared test or Fisher’s exact test, as appropriate, with Bonferroni adjustment. Differential analysis of liver transcriptomic across the clusters was performed using moderated t -tests from the R Bioconductor package Limma v.3.60.4. The same methodology was also applied to metabolomic after exclusion of xeniobiotics. Differences were considered statistically significant when P value(s) adjusted for multiple comparisons using the Benjamini–Hochberg correction (to control the false discovery rate) were less than 0.05 and the absolute value of log 2 fold change was greater than 0.26. Group comparisons for genes were represented using volcano plots. The number of differentially expressed genes between the various clusters were reported through Euler diagrams. Pathway enrichment on the transcriptome was performed with the R package ClusterProfiler (v.4.7.1), based on GO-BP pathways. The GSEA method was run with the absolute value of the moderated t -test statistic as ranking metric. The P values of enriched pathways were adjusted using the Benjamini–Hochberg procedure, and an adjusted P value <0.05 was considered significant. In the UK Biobank, clusters were compared using analysis of variance, Kruskal–Wallis test, chi-square test or Fisher’s test as appropriate, adjusted for multiple testing separately for clinical data and genetic data, using the Bonferroni method. Similarly, post hoc comparisons were carried out with Bonferroni correction. The incidence of chronic liver disease, cardiovascular disease and type 2 diabetes were defined as the composite occurrence of the clinical event or event-related death during follow-up. Then, the cumulative incidence of the clinical outcomes was computed according to the Aalen–Johansen method for chronic liver disease, cardiovascular disease and type 2 diabetes, taking into account the competing occurrence of other-cause death, and of selected liver disease (only in the case of chronic liver disease; see above for ICD-10 codes). Cause-specific HRs were calculated through Cox regressions, adjusted for age, sex and alcohol intake. The proportional hazard assumption was verified through the inspection of the Schoenfeld residuals. Sensitivity analyses were performed (1) including only individuals with BMI ≥27 kg m −2 and (2) excluding those with harmful alcohol consumption (>50/60 g per day for women/men). Statistical analyses and graphical representations were performed using R statistical software v.4.4.1 (R Foundation for Statistical Computing, Vienna, Austria).

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Extended Data Table 1 Patient characteristics based on cluster allocation in the ABOS cohort (n=1,389) Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Adj-p Cluster 1–6 N 256 158 180 361 99 335 − Clinical data Age (years) 53 (10) 52 (11.75) 34 (16) 46 (11) 37 (15) 30 (10) <0.001 Women n (%) 175 (68.4) 86 (54.4) 123 (68.3) 290 (80.3) 57 (55.6) 310 (92.5) <0.001 BMI (Kg/m2) 45.5 (7.5) 44.85 (9.4) 59.7 (7.93) 44.4 (7.2) 43.9 (6.5) 43.8 (5.9) <0.001 Waist circumference (cm) 140 (19.75) 134 (20.5) 161 (20.25) 138 (17) 137 (15) 139 (14) <0.001 Significant alcohol intake 1 (n) 1 9 (6.9) 7 (8.0) 6 (6.0) 15 (8.4) 3 (6.7) 12 (6.7) 1 Glucose profile HbA1c (%) 6.2 (1.03) 9.2 (2.28) 5.8 (0.8) 5.8 (0.7) 5.9 (1.05) 5.4 (0.5) <0.001 Fasting glucose (mmol/L) 6.1 (1.79) 10.24 (5.3) 5.49 (1.22) 5.55 (1.14) 5.83 (1.72) 5.11 (0.61) <0.001 Fasting insulin (UI/L) 2 13.9 (11.7) 15.1 (16.05) 16.75 (10.35) 13.7 (9.8) 19.65 (15.23) 13.7 (9.62) <0.001 Lipid profile Total cholesterol (mmol/L) 4.37 (0.84) 4.47 (1.33) 4.7 (0.96) 5.86 (0.86) 5.09 (0.89) 4.6 (0.89) <0.001 HDL cholesterol (mmol/L) 1.16 (0.36) 0.98 (0.29) 1.11 (0.31) 1.16 (0.31) 1.01 (0.31) 1.14 (0.34) <0.001 LDL cholesterol (mmol/L) 2.47 (0.75) 2.53 (1.05) 2.97 (0.81) 3.85 (0.7) 3.33 (0.9) 2.9 (0.77) <0.001 Triglycerides (mmol/L) 1.4 (0.77) 2.34 (1.56) 1.27 (0.68) 1.49 (0.73) 1.61 (0.8) 1.11 (0.6) <0.001 Liver function tests AST (UI/L) 22 (10) 30 (18) 22 (11) 23 (8) 44 (20.75) 21 (9) <0.001 ALT (UI/L) 25 (15) 39 (26) 26 (17) 26 (14) 75 (26.5) 21 (15) <0.001 GGT (UI/L) 31 (24.25) 58 (71.75) 28.5 (21.25) 30 (25) 53.5 (47.75) 22 (16) <0.001 Comorbidities Hypertension n (%) 201 (78.5) 138 (87.3) 109 (60.6) 200 (55.4) 55 (55.6) 107 (31.9) <0.001 Type 2 diabetes n (%) 140 (54.7) 156 (98.7) 50 (27.8) 98 (27.1) 41 (41.4) 23 (6.9) <0.001 Dyslipidemia n (%) 137 (53.5) 132 (83.5) 75 (41.7) 332 (92.0) 59 (59.6) 83 (24.8) <0.001 Medications Anti-hypertensive drugs n (%) 180 (70.3) 125 (79.1) 62 (34.4) 139 (38.5) 34 (34.3) 37 (11%) <0.001 Oral glucose-lowering drugs n (%) 122 (47.8) 148 (94.3) 34 (18.9) 63 (17.5) 29 (29.3) 14 (4.2) <0.001 Insulin n (%) 30 (11.8) 83 (52.5) 5 (2.8) 9 (2.5) 3 (3.0%) 2 (0.6) <0.001 Lipid-lowering drugs n (%) 112 (43.8) 95 (60.1) 18 (10.0) 52 (14.4) 10 (10.1) 9 (2.7) <0.001 Statins n (%) 104 (40.6) 81 (51.3) 11 (6.1) 42 (11.6) 5 (5.1) 8 (2.4) <0.001 Liver histology 3 Steatosis grade ≥ 1 n (%) 213 (85.9) 150 (97.4) 150 (85.2) 303 (85.8) 90 (92.8) 213 (64.5) <0.001 Lobular inflammation grade ≥ 1 n (%) 76 (31.4) 83 (54.6) 51 (30.4) 105 (30.1) 53 (55.8) 79 (24.6) <0.001 Ballooning grade ≥ 1 n (%) 29 (12.0) 59 (38.8) 20 (11.8) 23 (6.6) 24 (25.3) 15 (4.7) <0.001 MASH n (%) 16 (6.6) 51 (33.6) 14 (8.3) 16 (4.6) 23 (24.2) 8 (2.5) <0.001 Fibrosis stage ≥ 2 n (%) 26 (11.3) 49 (33.3) 22 (13.3) 21 (6.3) 19 (20.0) 12 (3.9) <0.001 Fibrosis stage 3-4 n (%) 15 (6.5) 32 (21.8) 7 (4.2) 9 (2.7) 15 (15.8) 4 (1.3) <0.001 NAS score 2 (2) 3 (3) 1 (2) 1 (1) 3 (2.5) 1 (2) <0.001 Genetics PNPLA3 rs738409 n (CC/CG+GG) 129 (54.9) 79 (57.7) 95 (59.0) 195 (59.1) 31 (36.0) 189 (61.0) 0.009 TM6SF2 rs58542926 n (CC/CT+TT) 197 (84.9) 118 (86.8) 147 (90.7) 298 (90.0) 69 (80.2) 273 (87.2) 0.42 MBOAT7 rs641738 n (CC/CT+TT) 74 (32.2) 38 (27.5) 48 (29.8) 109 (32.8) 21 (24.4) 104 (33.3) 1 GCKR rs1260326 n (CC/CT+TT) 76 (32.9) 41 (29.7) 54 (33.5) 111 (33.6) 23 (26.7) 105 (33.5) 1 PRS-HFC + 4 0.27 (0.27) 0.26 (0.27) 0.19 (0.33) 0.26 (0.27) 0.39 (0.41) 0.19 (0.27) <0.001 PRS-HFC− 5 0.13 (0.13) 0.13 (0.13) 0.13 (0.13) 0.13 (0.13) 0.13 (0.07) 0.13 (0.13) 1 Data were reported as median (interquartile range) for continuous variables and frequencies (percentages) for categorical variables. Clusters were compared using Kruskal-Wallis test, Chi-squared test, or Fisher’s exact test, as appropriate. Differences were considered statistically significant when p-value(s) adjusted for multiple comparisons using Bonferroni correction, performed separately for clinical data, histological data and genetic data, were less than 0.05. 1 Significant alcohol intake was defined as a daily consumption above 20 g in women and 30 g in men 2 Patients receiving insulin were excluded. 3 Liver histology was available from 1325 participants 4: PRS-HFC + Polygenic Risk Score was calculated with the formula: prs=0.266∗PNPLA3_012 + 0.274∗TMS6F2_012 + 0.065∗GCKR_012 + 0.063∗MBOAT7_012 5: PRS-HFC - Polygenic Risk Score was calculated without PNPLA3 with the formula: prs=0.274∗TMS6F2_012 + 0.065∗GCKR_012 + 0.063∗MBOAT7_012 Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; eGFR, estimates of glomerular filtration rate; GCKR, glucokinase regulator; GGT, gamma glutamyltransferase; HbA1c, hemoglobin A1c; HDL, high-density lipoprotein; HOMA2-B, homeostasis model assessment 2 estimates of beta-cell function; HOMA2-IR, homeostasis model assessment 2 estimates of insulin-resistance; LDL, low-den

The online version contains supplementary material available at 10.1038/s41591-024-03283-1.