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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/m(2)]. 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 BM Is <30.0, 30.0 to 39.9, and >= 40.0 kg/m(2). 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 AD, Funding Agencies|Academy of FinlandAcademy of Finland [309263]; Novo Nordisk FoundationNovo Nordisk FoundationNovocure Limited; Sigrid Juselius FoundationSigrid Juselius Foundation; Orion Research Foundation sr; Yrjo Jahnsson Foundation sr; Maud Kuistila Memorial Foundation sr; Emil Aaltonen Foundation sr; Italian Ministry of Health (Ministero della Salute, Ricerca Finalizzata) [RF-2016-02364358]; Ricerca Corrente Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico; Fondazione IRCCS Ca Granda core COVID-19 Biobank [RC100017A]; Fondazione IRCCS Ca Granda "Liver BIBLE" [PR-0391]

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/m(2)]. 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 BM Is <30.0, 30.0 to 39.9, and >= 40.0 kg/m(2). 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 AD, Funding Agencies|Academy of FinlandAcademy of Finland [309263]; Novo Nordisk FoundationNovo Nordisk FoundationNovocure Limited; Sigrid Juselius FoundationSigrid Juselius Foundation; Orion Research Foundation sr; Yrjo Jahnsson Foundation sr; Maud Kuistila Memorial Foundation sr; Emil Aaltonen Foundation sr; Italian Ministry of Health (Ministero della Salute, Ricerca Finalizzata) [RF-2016-02364358]; Ricerca Corrente Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico; Fondazione IRCCS Ca Granda core COVID-19 Biobank [RC100017A]; Fondazione IRCCS Ca Granda "Liver BIBLE" [PR-0391]

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/m(2)]. 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 BM Is <30.0, 30.0 to 39.9, and >= 40.0 kg/m(2). 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 AD, Funding Agencies|Academy of FinlandAcademy of Finland [309263]; Novo Nordisk FoundationNovo Nordisk FoundationNovocure Limited; Sigrid Juselius FoundationSigrid Juselius Foundation; Orion Research Foundation sr; Yrjo Jahnsson Foundation sr; Maud Kuistila Memorial Foundation sr; Emil Aaltonen Foundation sr; Italian Ministry of Health (Ministero della Salute, Ricerca Finalizzata) [RF-2016-02364358]; Ricerca Corrente Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico; Fondazione IRCCS Ca Granda core COVID-19 Biobank [RC100017A]; Fondazione IRCCS Ca Granda "Liver BIBLE" [PR-0391]

BACKGROUND AND AIMS: The susceptibility of low-density lipoprotein (LDL) to aggregation predicts atherosclerotic cardiovascular disease. However, causes of interindividual variation in LDL lipid composition and aggregation susceptibility remain unclear. We examined whether the lipid composition and aggregation susceptibility of LDL reflect the lipid composition of the human liver. METHODS: Liver biopsies and blood samples for isolation of LDL particles were obtained from 40 obese subjects (BMI 45.9 ± 6.1 kg/m2, age 43 ± 8 years). LDL was isolated using sequential ultracentrifugation and lipidomic analyses of liver and LDL samples were determined using ultra-high performance liquid chromatography-mass spectrometry. LDL aggregation susceptibility ex vivo was analyzed by inducing aggregation by human recombinant secretory sphingomyelinase and following aggregate formation. RESULTS: The composition (acyl carbon number and double bond count) of hepatic triglycerides, phosphatidylcholines, and sphingomyelins (SMs) was closely associated with that of LDL particles. Hepatic dihydroceramides and ceramides were positively correlated with concentrations of the corresponding SM species in LDL as well with LDL aggregation. These relationships remained statistically significant after adjustment for age, sex, and body mass index. CONCLUSIONS: Lipid composition of LDL reflects that of the human liver in obese patients. Changes in hepatic sphingolipid metabolism may contribute to interindividual variation of LDL lipid composition and susceptibility to aggregation.

Background and aims: In murine models of non-alcoholic fatty liver disease (NAFLD), liver damage associates with a deficiency of phosphatidylcholines (PCs), particularly polyunsaturated PCs (PUFA-PCs). We studied whether human PC metabolism is altered by NAFLD or by the protective genetic variant in HSD17B13 (rs72613567 T > TA). Method: In 143 obese patients with a liver biopsy and genotyping for HSD17B13 rs72613567, we analysed the hepatic lipidome (UPLC-MS). As the hepatic parenchymal fat fraction (HPFF) affects apparent concentrations of amphiphilic lipids, we normalised hepatic phospholipid concentrations to fat-free liver mass. To this end, we employed a state-of-the-art deep learning image analysis method (Aiforia Technologies) to accurately quantify HPFF in liver biopsies. Results: Total unadjusted hepatic PCs correlated negatively with HPFF (rs = −0.26, P < 0.01), but this association disappeared after normalising to fat-free liver mass (rs = 0.02, P = 0.81). With increasing HPFF, concentrations of especially saturated and monounsaturated PCs significantly increased, whereas concentrations of PUFA-PCs decreased. Accordingly, the hepatic triacylglycerol composition significantly correlated with that of hepatic PCs. In carriers of the protective variant in HSD17B13, as compared to non-carriers, the hepatic lipidome was enriched in especially PUFA-PCs. Conclusion: Patients with NAFLD have a deficiency of PUFA-PCs. The protective HSD17B13 rs72613567 variant opposes these changes, increasing intrahepatic PC concentrations.

Background and aims: Non-alcoholic fatty liver disease (NAFLD) is associated with increased circulating bile acids (BAs). It is unknown whether this reflects altered intrahepatic BA metabolism due to NAFLD or the associated insulin resistance (IR). To dissociate steatosis from IR, we compared BA metabolism in NAFLD associated with either IR or high genetic risk. Method: In 106 patients undergoing a liver biopsy, we analysed serum/liver BAs, the hepatic transcriptome (RNA-seq), and concentrations of plasma FGF-19 (marker of intestinal BA metabolism). Using HOMA-IR and a validated weighted Polygenic Risk Score (PRS) for NAFLD, we divided the patients into matched groups to compare the effects of NAFLD associated with IR (‘High HOMA-IR’ vs. ‘LowHOMA-IR’) or with high genetic risk (‘High PRS’ vs. ‘Low PRS’) on BA metabolism. Results: An untargeted analysis identified distinct clusters of patients with simultaneously increased BAs, HOMA-IR, and liver fat content. Compared to ‘Low HOMA-IR’, patients with ‘High HOMA-IR’ had significantly higher total (+57%, P = 0.011) and especially conjugated (+82%, P = 0.002) serum BAs, but unchanged hepatic BAs. Expression of the primary hepatic BA uptake transporter NTCP was down-regulated, while plasma FGF-19 was unchanged. Despite having the same degree of steatosis and NASH compared to the ‘High HOMA-IR’ group, patients with ‘High PRS’ had similar serum/liver BAs compared to those with ‘Low PRS’. Stage F3-F4 liver fibrosis independently predicted higher serum BAs. Conclusion: In NAFLD without advanced fibrosis, serum BAs are increased due to IR, which may impair hepatocellular BA uptake. Intrahepatic BAs are unchanged in NAFLD.

Background & aims: Recent experimental models and epidemiological studies suggest that specific environmental contaminants (ECs) contribute to the initiation and pathology of non-alcoholic fatty liver disease (NAFLD). However, the underlying mechanisms linking EC exposure with NAFLD remain poorly understood and there is no data on their impact on the human liver metabolome. Herein, we hypothesized that exposure to ECs, particularly perfluorinated alkyl substances (PFAS), impacts liver metabolism, specifically bile acid metabolism. Methods: In a well-characterized human NAFLD cohort of 105 individuals, we investigated the effects of EC exposure on liver metabolism. We characterized the liver (via biopsy) and circulating metabolomes using 4 mass spectrometry-based analytical platforms, and measured PFAS and other ECs in serum. We subsequently compared these results with an exposure study in a PPARa-humanized mouse model. Results: PFAS exposure appears associated with perturbation of key hepatic metabolic pathways previously found altered in NAFLD, particularly those related to bile acid and lipid metabolism. We identified stronger associations between the liver metabolome, chemical exposure and NAFLD-associated clinical variables (liver fat content, HOMA-IR), in females than males. Specifically, we observed PFAS-associated upregulation of bile acids, triacylglycerols and ceramides, and association between chemical exposure and dysregulated glucose metabolism in females. The murine exposure study further corroborated our findings, vis-à-vis a sex-specific association between PFAS exposure and NAFLD-associated lipid changes. Conclusions: Females may be more sensitive to the harmful impacts of PFAS. Lipid-related changes subsequent to PFAS exposure may be secondary to the interplay between PFAS and bile acid metabolism. Lay summary: There is increasing evidence that specific environmental contaminants, such as perfluorinated alkyl substances (PFAS), contribute to the, Funding agencies:Juselius FoundationUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Environmental Health Sciences (NIEHS) P42 ES007381 R01 ES027813

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/m(2)]. 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 BM Is <30.0, 30.0 to 39.9, and >= 40.0 kg/m(2). 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 AD, Funding Agencies|Academy of FinlandAcademy of Finland [309263]; Novo Nordisk FoundationNovo Nordisk FoundationNovocure Limited; Sigrid Juselius FoundationSigrid Juselius Foundation; Orion Research Foundation sr; Yrjo Jahnsson Foundation sr; Maud Kuistila Memorial Foundation sr; Emil Aaltonen Foundation sr; Italian Ministry of Health (Ministero della Salute, Ricerca Finalizzata) [RF-2016-02364358]; Ricerca Corrente Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico; Fondazione IRCCS Ca Granda core COVID-19 Biobank [RC100017A]; Fondazione IRCCS Ca Granda "Liver BIBLE" [PR-0391]

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/m(2)]. 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 BM Is <30.0, 30.0 to 39.9, and >= 40.0 kg/m(2). 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 AD, Funding Agencies|Academy of FinlandAcademy of Finland [309263]; Novo Nordisk FoundationNovo Nordisk FoundationNovocure Limited; Sigrid Juselius FoundationSigrid Juselius Foundation; Orion Research Foundation sr; Yrjo Jahnsson Foundation sr; Maud Kuistila Memorial Foundation sr; Emil Aaltonen Foundation sr; Italian Ministry of Health (Ministero della Salute, Ricerca Finalizzata) [RF-2016-02364358]; Ricerca Corrente Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico; Fondazione IRCCS Ca Granda core COVID-19 Biobank [RC100017A]; Fondazione IRCCS Ca Granda "Liver BIBLE" [PR-0391]

OBJECTIVE: We recently showed that measurement of the susceptibility of LDL (low-density lipoprotein) to aggregation is an independent predictor of cardiovascular events. We now wished to compare effects of overfeeding different dietary macronutrients on LDL aggregation, proteoglycan-binding of plasma lipoproteins, and on the concentration of oxidized LDL in plasma, 3 in vitro parameters consistent with increased atherogenicity. Approach and Results: The participants (36 subjects; age, 48±10 years; body mass index, 30.9±6.2 kg/m2) were randomized to consume an extra 1000 kcal/day of either unsaturated fat, saturated fat, or simple sugars (CARB) for 3 weeks. We measured plasma proatherogenic properties (susceptibility of LDL to aggregation, proteoglycan-binding, oxidized LDL) and concentrations and composition of plasma lipoproteins using nuclear magnetic resonance spectroscopy, and in LDL using liquid chromatography mass spectrometry, before and after the overfeeding diets. LDL aggregation increased in the saturated fat but not the other groups. This change was associated with increased sphingolipid and saturated triacylglycerols in LDL and in plasma and reduction of clusterin on LDL particles. Proteoglycan binding of plasma lipoproteins decreased in the unsaturated fat group relative to the baseline diet. Lipoprotein properties remained unchanged in the CARB group. CONCLUSIONS: The type of fat during 3 weeks of overfeeding is an important determinant of the characteristics and functional properties of plasma lipoproteins in humans. REGISTRATION: URL: http://www.clinicaltrials.gov; Unique identifier NCT02133144., Funding agencies:Jenny and Antti Wihuri FoundationFinnish Foundation for Cardiovascular ResearchBritish Heart Foundation Senior Research Fellowship FS/15/56/31645Alfred Kordelin Foundation Jalmari and Rauha Ahokas FoundationPaulo FoundationFinnish Medical FoundationSigrid Juselius FoundationEVO foundation

BACKGROUND & AIMS: The I148M variant in PNPLA3 is the major genetic risk factor for non-alcoholic fatty liver disease (NAFLD). The liver is enriched with polyunsaturated triglycerides (PUFA-TGs) in PNPLA3-I148M carriers. Gene expression data indicate that PNPLA3 is liver-specific in humans, but whether it functions in adipose tissue (AT) is unknown. We investigated whether PNPLA3-I148M modifies AT metabolism in human NAFLD. METHODS: Profiling of the AT lipidome and fasting serum non-esterified fatty acid (NEFA) composition were conducted in 125 volunteers (PNPLA3148MM/MI , n=63; PNPLA3148II , n=62). AT fatty acid composition was determined in 50 volunteers homozygous for the variant (PNPLA3148MM , n=25) or lacking the variant (PNPLA3148II , n=25). Whole-body insulin sensitivity of lipolysis was determined using [2 H5 ]glycerol, and PNPLA3 mRNA and protein levels were measured in subcutaneous AT and liver biopsies in a subset of the volunteers. RESULTS: PUFA-TGs were significantly increased in AT in carriers versus non-carriers of PNPLA3-I148M. The variant did not alter the rate of lipolysis or the composition of fasting serum NEFAs. PNPLA3 mRNA was 33-fold higher in the liver than in AT (p<0.0001). In contrast, PNPLA3 protein levels per tissue protein were 3-fold higher in AT than the liver (p<0.0001) and 9-fold higher when related to whole-body AT and liver tissue masses (p<0.0001). CONCLUSIONS: Contrary to previous assumptions, PNPLA3 is highly abundant in AT. PNPLA3-I148M locally remodels AT TGs to become polyunsaturated as it does in the liver, without affecting lipolysis or composition of serum NEFAs. Changes in AT metabolism do not contribute to NAFLD in PNPLA3-I148M carriers., Funding Agencies:European Union (EU) 634413777377-2British Heart Foundation FS/15/56/31645Sigrid Juselius FoundationAcademy of Finland 309263EVO