Surveillance of the liver in type 2 diabetes: important but unfeasible?

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.

Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information.

AIMS: Metabolic dysfunction Associated Steatotic Liver Disease (MASLD) outcomes such as MASH (metabolic dysfunction associated steatohepatitis), fibrosis and cirrhosis are ordinarily determined by resource-intensive and invasive biopsies. We aim to show that routine clinical tests offer sufficient information to predict these endpoints. METHODS: Using the LITMUS Metacohort derived from the European NAFLD Registry, the largest MASLD dataset in Europe, we create three combinations of features which vary in degree of procurement including a 19-variable feature set that are attained through a routine clinical appointment or blood test. This data was used to train predictive models using supervised machine learning (ML) algorithm XGBoost, alongside missing imputation technique MICE and class balancing algorithm SMOTE. Shapley Additive exPlanations (SHAP) were added to determine relative importance for each clinical variable. RESULTS: Analysing nine biopsy-derived MASLD outcomes of cohort size ranging between 5385 and 6673 subjects, we were able to predict individuals at training set AUCs ranging from 0.719-0.994, including classifying individuals who are At-Risk MASH at an AUC = 0.899. Using two further feature combinations of 26-variables and 35-variables, which included composite scores known to be good indicators for MASLD endpoints and advanced specialist tests, we found predictive performance did not sufficiently improve. We are also able to present local and global explanations for each ML model, offering clinicians interpretability without the expense of worsening predictive performance. CONCLUSIONS: This study developed a series of ML models of accuracy ranging from 71.9-99.4% using only easily extractable and readily available information in predicting MASLD outcomes which are usually determined through highly invasive means.

IL32 downregulation lowers triglycerides and type I collagen in di-lineage human primary liver organoids.

Steatotic liver disease (SLD) prevails as the most common chronic liver disease yet lack approved treatments due to incomplete understanding of pathogenesis. Recently, elevated hepatic and circulating interleukin 32 (IL-32) levels were found in individuals with severe SLD. However, the mechanistic link between IL-32 and intracellular triglyceride metabolism remains to be elucidated. We demonstrate in vitro that incubation with IL-32ß protein leads to an increase in intracellular triglyceride synthesis, while downregulation of IL32 by small interfering RNA leads to lower triglyceride synthesis and secretion in organoids from human primary hepatocytes. This reduction requires the upregulation of Phospholipase A2 group IIA (PLA2G2A). Furthermore, downregulation of IL32 results in lower intracellular type I collagen levels in di-lineage human primary hepatic organoids. Finally, we identify a genetic variant of IL32 (rs76580947) associated with lower circulating IL-32 and protection against SLD measured by non-invasive tests. These data suggest that IL32 downregulation may be beneficial against SLD.

The first human normative ranges and biomarker performance of dimethylguanidino valeric acid isoforms in fatty liver disease.

We have recently determined dimethylguanidino valeric acid (DMGV) to be a novel biomarker of liver injury in non-alcoholic fatty liver disease (NAFLD) and an independent predictor of incident diabetes over a decade in advance. DMGV consists of two stereo-isomers, asymmetric dimethylguanidino valeric acid (ADGV) and symmetric dimethylguanidino valeric acid (SDGV). Here we report, for the first time, the upper limits of normal of both isomers in humans at the accepted 5.56% liver fat threshold for NAFLD, determined using in vivo magnetic resonance spectroscopy. We performed independent and blinded comparative analyses of ADGV and SDGV levels using two different liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods in (A) our laboratory, and (B) the New South Wales Chemical Pathology state laboratory, using unique columns, LC-MS/MS equipment, extraction protocols and normalisation approaches. Despite these differences, each laboratory reported consistent absolute concentrations across a range of liver fat percentages. We next determined the diagnostic performance of SDGV compared to ADGV in a cohort of 268 individuals with liver fat measurements. In derivation-validation analyses we determined rule-in/rule-out thresholds and the concentration of SDGV that provides optimal performance across sensitivity and specificity for the identification of NAFLD. In conclusion, we have herein determined for the first time the true human plasma reference range of both isoforms of an emerging novel biomarker of NAFLD, at the accepted upper normal threshold of liver fat. We have also identified that SDGV is the isoform with the best diagnostic performance and determined the optimal cut-point for its detection of NAFLD.

Development and Validation of the Nonalcoholic Fatty Liver Disease Familial Risk Score to Detect Advanced Fibrosis: A Prospective, Multicenter Study.

BACKGROUND & AIMS: Nonalcoholic fatty liver disease (NAFLD)-related fibrosis is heritable, but it is unclear how family history may be used to identify first-degree relatives with advanced fibrosis. We aimed to develop and validate a simple risk score to identify first-degree relatives of probands who have undergone assessment of liver fibrosis who are at higher risk of NAFLD with advanced fibrosis. METHODS: This prospective, cross-sectional, familial study consisted of a derivation cohort from San Diego, California, and a validation cohort from Helsinki, Finland. This study included consecutive adult probands (n = 242) with NAFLD and advanced fibrosis, NAFLD without advanced fibrosis, and non-NAFLD, with at least 1 of their first-degree relatives. All included probands and first-degree relatives underwent evaluation of liver fibrosis, the majority by magnetic resonance elastography. RESULTS: A total of 396 first-degree relatives (64% male) were included. The median age and body mass index were 47 years (interquartile range, 32-62 y) and 27.6 kg/m2 (interquartile range, 24.1-32.5 kg/m2), respectively. Age (1 point), type 2 diabetes (1 point), obesity (2 points), and proband with NAFLD and advanced fibrosis (2 points) were predictors of advanced fibrosis among first-degree relatives in the derivation cohort (n = 220) and formed the NAFLD Familial Risk Score. The area under the receiver operator characteristic curve of the NAFLD Familial Risk Score for detecting advanced fibrosis was 0.94 in the validation cohort (n = 176). The NAFLD Familial Risk Score outperformed the Fibrosis-4 index in the validation cohort (area under the receiver operator characteristic curve, 0.94 vs 0.70; P = .02). CONCLUSIONS: The NAFLD Familial Risk Score is a simple and accurate clinical tool to identify advanced fibrosis in first-degree relatives. These data may have implications for surveillance in NAFLD.

Marked difference in liver fat measured by histology vs. magnetic resonance-proton density fat fraction: A meta-analysis.

Background & Aims: 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. Methods: 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. Results: 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. Conclusions: 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. Impact and implications: 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.

The PNPLA3 I148M variant increases ketogenesis and decreases hepatic de novo lipogenesis and mitochondrial function in humans.

The PNPLA3 I148M variant is the major genetic risk factor for all stages of fatty liver disease, but the underlying pathophysiology remains unclear. We studied the effect of this variant on hepatic metabolism in homozygous carriers and non-carriers under multiple physiological conditions with state-of-the-art stable isotope techniques. After an overnight fast, carriers had higher plasma ß-hydroxybutyrate concentrations and lower hepatic de novo lipogenesis (DNL) compared to non-carriers. After a mixed meal, fatty acids were channeled toward ketogenesis in carriers, which was associated with an increase in hepatic mitochondrial redox state. During a ketogenic diet, carriers manifested increased rates of intrahepatic lipolysis, increased plasma ß-hydroxybutyrate concentrations, and decreased rates of hepatic mitochondrial citrate synthase flux. These studies demonstrate that homozygous PNPLA3 I148M carriers have hepatic mitochondrial dysfunction leading to reduced DNL and channeling of carbons to ketogenesis. These findings have implications for understanding why the PNPLA3 variant predisposes to progressive liver disease.

Liver Investigation: Testing Marker Utility in Steatohepatitis (LITMUS): Assessment & validation of imaging modality performance across the NAFLD spectrum in a prospectively recruited cohort study (the LITMUS imaging study): Study protocol.

Non-alcoholic fatty liver disease (NAFLD) is the liver manifestation of the metabolic syndrome with global prevalence reaching epidemic levels. Despite the high disease burden in the population only a small proportion of those with NAFLD will develop progressive liver disease, for which there is currently no approved pharmacotherapy. Identifying those who are at risk of progressive NAFLD currently requires a liver biopsy which is problematic. Firstly, liver biopsy is invasive and therefore not appropriate for use in a condition like NAFLD that affects a large proportion of the population. Secondly, biopsy is limited by sampling and observer dependent variability which can lead to misclassification of disease severity. Non-invasive biomarkers are therefore needed to replace liver biopsy in the assessment of NAFLD. Our study addresses this unmet need. The LITMUS Imaging Study is a prospectively recruited multi-centre cohort study evaluating magnetic resonance imaging and elastography, and ultrasound elastography against liver histology as the reference standard. Imaging biomarkers and biopsy are acquired within a 100-day window. The study employs standardised processes for imaging data collection and analysis as well as a real time central monitoring and quality control process for all the data submitted for analysis. It is anticipated that the high-quality data generated from this study will underpin changes in clinical practice for the benefit of people with NAFLD. Study Registration: clinicaltrials.gov: NCT05479721.

Programmed cell death 1 genetic variant and liver damage in nonalcoholic fatty liver disease.

BACKGROUND AND AIMS: Programmed cell death 1/programmed cell death-ligand 1 (PD-1/PDL-1) axis has been reported to modulate liver inflammation and progression to hepatocellular carcinoma (HCC) in patients with nonalcoholic fatty liver disease (NAFLD). Here, we examined whether the PDCD1 variation is associated with NAFLD severity in individuals with liver biopsy. METHODS: We examined the impact of PDCD1 gene variants on HCC, as robust severe liver disease phenotype in UK Biobank participants. The strongest genetic association with the rs13023138 G>C variation was subsequently tested for association with liver damage in 2889 individuals who underwent liver biopsy for suspected nonalcoholic steatohepatitis (NASH). Hepatic transcriptome was examined by RNA-Seq in a subset of NAFLD individuals (n = 121). Transcriptomic and deconvolution analyses were performed to identify biological pathways modulated by the risk allele. RESULTS: The rs13023138 C>G showed the most robust association with HCC in UK Biobank (p = 5.28E-4, OR = 1.32, 95% CI [1.1, 1.5]). In the liver biopsy cohort, rs13023138 G allele was independently associated with severe steatosis (OR 1.17, 95% CI 1.02-1.34; p = .01), NASH (OR 1.22, 95% CI 1.09-1.37; p < .001) and advanced fibrosis (OR 1.26, 95% CI 1.06-1.50; p = .007). At deconvolution analysis, rs13023138 G>C allele was linked to higher hepatic representation of M1 macrophages, paralleled by upregulation of pathways related to inflammation and higher expression of CXCR6. CONCLUSIONS: The PDCD1 rs13023138 G allele was associated with HCC development in the general population and with liver disease severity in patients at high risk of NASH.

Validation of the Blood Test MACK-3 for the Noninvasive Diagnosis of Fibrotic Nonalcoholic Steatohepatitis: An International Study With 1924 Patients.

BACKGROUND & AIMS: Drug development in nonalcoholic steatohepatitis (NASH) is hampered by a high screening failure rate that reaches 60% to 80% in therapeutic trials, mainly because of the absence of fibrotic NASH on baseline liver histology. MACK-3, a blood test including 3 biomarkers (aspartate aminotransferase, homeostasis model assessment, and cytokeratin 18), recently was developed for the noninvasive diagnosis of fibrotic NASH. We aimed to validate the diagnostic accuracy of this noninvasive test in an international multicenter study. METHODS: A total of 1924 patients with biopsy-proven nonalcoholic fatty liver disease from 10 centers in Asia, Australia, and Europe were included. The blood test MACK-3 was calculated for all patients. FibroScan-aspartate aminotransferase score (FAST), an elastography-based test for fibrotic NASH, also was available in a subset of 655 patients. Fibrotic NASH was defined as the presence of NASH on liver biopsy with a Nonalcoholic Fatty Liver Disease Activity Score of 4 or higher and fibrosis stage of F2 or higher according to the NASH Clinical Research Network scoring system. RESULTS: The area under the receiver operating characteristic of MACK-3 for fibrotic NASH was 0.791 (95% CI 0.768-0.814). Sensitivity at the previously published MACK-3 threshold of less than 0.135 was 91% and specificity at a greater than 0.549 threshold was 85%. The MACK-3 area under the receiver operating characteristic was not affected by age, sex, diabetes, or body mass index. MACK-3 and FAST results were well correlated (Spearman correlation coefficient, 0.781; P < .001). Except for an 8% higher rate of patients included in the grey zone, MACK-3 provided similar accuracy to that of FAST. Both tests included 27% of patients in their rule-in zone, with 85% specificity and 35% false positives (screen failure rate). CONCLUSIONS: The blood test MACK-3 is an accurate tool to improve patient selection in NASH therapeutic trials.

Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study.

BACKGROUND AND AIMS: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. APPROACH AND RESULTS: Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS ≥ 4;53%), at-risk NASH (NASH with F ≥ 2;35%), significant (F ≥ 2;47%), and advanced fibrosis (F ≥ 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models.Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). CONCLUSIONS: Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis.

Biomarkers for staging fibrosis and non-alcoholic steatohepatitis in non-alcoholic fatty liver disease (the LITMUS project): a comparative diagnostic accuracy study.

BACKGROUND: The reference standard for detecting non-alcoholic steatohepatitis (NASH) and staging fibrosis-liver biopsy-is invasive and resource intensive. Non-invasive biomarkers are urgently needed, but few studies have compared these biomarkers in a single cohort. As part of the Liver Investigation: Testing Marker Utility in Steatohepatitis (LITMUS) project, we aimed to evaluate the diagnostic accuracy of 17 biomarkers and multimarker scores in detecting NASH and clinically significant fibrosis in patients with non-alcoholic fatty liver disease (NAFLD) and identify their optimal cutoffs as screening tests in clinical trial recruitment. METHODS: This was a comparative diagnostic accuracy study in people with biopsy-confirmed NAFLD from 13 countries across Europe, recruited between Jan 6, 2010, and Dec 29, 2017, from the LITMUS metacohort of the prospective European NAFLD Registry. Adults (aged ≥18 years) with paired liver biopsy and serum samples were eligible; those with excessive alcohol consumption or evidence of other chronic liver diseases were excluded. The diagnostic accuracy of the biomarkers was expressed as the area under the receiver operating characteristic curve (AUC) with liver histology as the reference standard and compared with the Fibrosis-4 index for liver fibrosis (FIB-4) in the same subgroup. Target conditions were the presence of NASH with clinically significant fibrosis (ie, at-risk NASH; NAFLD Activity Score ≥4 and F≥2) or the presence of advanced fibrosis (F≥3), analysed in all participants with complete data. We identified thres holds for each biomarker for reducing the number of biopsy-based screen failures when recruiting people with both NASH and clinically significant fibrosis for future trials. FINDINGS: Of 1430 participants with NAFLD in the LITMUS metacohort with serum samples, 966 (403 women and 563 men) were included after all exclusion criteria had been applied. 335 (35%) of 966 participants had biopsy-confirmed NASH and clinically significant fibrosis and 271 (28%) had advanced fibrosis. For people with NASH and clinically significant fibrosis, no single biomarker or multimarker score significantly reached the predefined AUC 0·80 acceptability threshold (AUCs ranging from 0·61 [95% CI 0·54-0·67] for FibroScan controlled attenuation parameter to 0·81 [0·75-0·86] for SomaSignal), with accuracy mostly similar to FIB-4. Regarding detection of advanced fibrosis, SomaSignal (AUC 0·90 [95% CI 0·86-0·94]), ADAPT (0·85 [0·81-0·89]), and FibroScan liver stiffness measurement (0·83 [0·80-0·86]) reached acceptable accuracy. With 11 of 17 markers, histological screen failure rates could be reduced to 33% in trials if only people who were marker positive had a biopsy for evaluating eligibility. The best screening performance for NASH and clinically significant fibrosis was observed for SomaSignal (number needed to test [NNT] to find one true positive was four [95% CI 4-5]), then ADAPT (six [5-7]), MACK-3 (seven [6-8]), and PRO-C3 (nine [7-11]). INTERPRETATION: None of the single markers or multimarker scores achieved the predefined acceptable AUC for replacing biopsy in detecting people with both NASH and clinically significant fibrosis. However, several biomarkers could be applied in a prescreening strategy in clinical trial recruitment. The performance of promising markers will be further evaluated in the ongoing prospective LITMUS study cohort. FUNDING: The Innovative Medicines Initiative 2 Joint Undertaking.

Mapping transcriptional heterogeneity and metabolic networks in fatty livers at single-cell resolution.

Non-alcoholic fatty liver disease is a heterogeneous disease with unclear underlying molecular mechanisms. Here, we perform single-cell RNA sequencing of hepatocytes and hepatic non-parenchymal cells to map the lipid signatures in mice with non-alcoholic fatty liver disease (NAFLD). We uncover previously unidentified clusters of hepatocytes characterized by either high or low srebp1 expression. Surprisingly, the canonical lipid synthesis driver Srebp1 is not predictive of hepatic lipid accumulation, suggestive of other drivers of lipid metabolism. By combining transcriptional data at single-cell resolution with computational network analyses, we find that NAFLD is associated with high constitutive androstane receptor (CAR) expression. Mechanistically, CAR interacts with four functional modules: cholesterol homeostasis, bile acid metabolism, fatty acid metabolism, and estrogen response. Nuclear expression of CAR positively correlates with steatohepatitis in human livers. These findings demonstrate significant cellular differences in lipid signatures and identify functional networks linked to hepatic steatosis in mice and humans.

Inhibition of HSD17B13 protects against liver fibrosis by inhibition of pyrimidine catabolism in nonalcoholic steatohepatitis.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, in which prognosis is determined by liver fibrosis. A common variant in hydroxysteroid 17-beta dehydrogenase 13 (HSD17B13, rs72613567-A) is associated with a reduced risk of fibrosis in NAFLD, but the underlying mechanism(s) remains unclear. We investigated the effects of this variant in the human liver and in Hsd17b13 knockdown in mice by using a state-of-the-art metabolomics approach. We demonstrate that protection against liver fibrosis conferred by the HSD17B13 rs72613567-A variant in humans and by the Hsd17b13 knockdown in mice is associated with decreased pyrimidine catabolism at the level of dihydropyrimidine dehydrogenase. Furthermore, we show that hepatic pyrimidines are depleted in two distinct mouse models of NAFLD and that inhibition of pyrimidine catabolism by gimeracil phenocopies the HSD17B13-induced protection against liver fibrosis. Our data suggest pyrimidine catabolism as a therapeutic target against the development of liver fibrosis in NAFLD.

Development and Validation of a Score for Fibrotic Nonalcoholic Steatohepatitis.

BACKGROUND & AIMS: Noninvasive assessment of histological features of nonalcoholic fatty liver disease (NAFLD) has been an intensive research area over the last decade. Herein, we aimed to develop a simple noninvasive score using routine laboratory tests to identify, among individuals at high risk for NAFLD, those with fibrotic nonalcoholic steatohepatitis (NASH) defined as NASH, NAFLD activity score ≥4, and fibrosis stage ≥2. METHODS: The derivation cohort included 264 morbidly obese individuals undergoing intraoperative liver biopsy in Rome, Italy. The best predictive model was developed and internally validated using a bootstrapping stepwise logistic regression analysis (2000 bootstrap samples). Performance was estimated by the area under the receiver operating characteristic curve (AUROC). External validation was assessed in 3 independent European cohorts (Finland, n = 370; Italy, n = 947; England, n = 5368) of individuals at high risk for NAFLD. RESULTS: The final predictive model, designated as Fibrotic NASH Index (FNI), combined aspartate aminotransferase, high-density lipoprotein cholesterol, and hemoglobin A1c. The performance of FNI for fibrotic NASH was satisfactory in both derivation and external validation cohorts (AUROC = 0.78 and AUROC = 0.80-0.95, respectively). In the derivation cohort, rule-out and rule-in cutoffs were 0.10 for sensitivity ≥0.89 (negative predictive value, 0.93) and 0.33 for specificity ≥0.90 (positive predictive value, 0.57), respectively. In the external validation cohorts, sensitivity ranged from 0.87 to 1 (negative predictive value, 0.99-1) and specificity from 0.73 to 0.94 (positive predictive value, 0.12-0.49) for rule-out and rule-in cutoff, respectively. CONCLUSION: FNI is an accurate, simple, and affordable noninvasive score which can be used to screen for fibrotic NASH in individuals with dysmetabolism in primary health care.

The quest for the missing links in fatty liver genetics: Deep learning to the rescue!

Park, MacLean, et al. conduct an exome-wide association study of liver fat content in the Penn Medicine BioBank.1 By leveraging machine learning-assisted analysis of clinical CT scans to quantify steatosis, they uncover previously undescribed liver fat-associated genetic variants.

The human liver lipidome is significantly related to the lipid composition and aggregation susceptibility of low-density lipoprotein (LDL) particles.

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.

Risk of advanced fibrosis in first-degree relatives of patients with nonalcoholic fatty liver disease.

BACKGROUNDA pilot, single-center study showed that first-degree relatives of probands with nonalcoholic fatty liver disease (NAFLD) cirrhosis have a high risk of advanced fibrosis. We aimed to validate these findings using 2 independent cohorts from the US and Europe.METHODSThis prospective study included probands with NAFLD with advanced fibrosis, NAFLD without advanced fibrosis, and non-NAFLD, with at least 1 first-degree relative. A total of 396 first-degree relatives - 220 in a derivation cohort and 176 in a validation cohort - were enrolled in the study, and liver fibrosis was evaluated using magnetic resonance elastography and other noninvasive imaging modalities. The primary outcome was prevalence of advanced fibrosis in first-degree relatives.RESULTSPrevalence of advanced fibrosis in first-degree relatives of probands with NAFLD with advanced fibrosis, NAFLD without advanced fibrosis, and non-NAFLD was 15.6%, 5.9%, and 1.3%, respectively (P = 0.002), in the derivation cohort, and 14.0%, 2.6%, and 1.3%, respectively (P = 0.004), in the validation cohort. In multivariable-adjusted logistic regression models, age of ≥50 years (adjusted OR [aOR]: 2.63, 95% CI 1.0-6.7), male sex (aOR: 3.79, 95% CI 1.6-9.2), diabetes mellitus (aOR: 3.37, 95% CI 1.3-9), and a first-degree relative with NAFLD with advanced fibrosis (aOR: 11.8, 95% CI 2.5-57) were significant predictors of presence of advanced fibrosis (all P < 0.05).CONCLUSIONFirst-degree relatives of probands with NAFLD with advanced fibrosis have significantly increased risk of advanced fibrosis. Routine screening should be done in the first-degree relatives of patients with advanced fibrosis.FUNDINGSupported by NCATS (5UL1TR001442), NIDDK (U01DK061734, U01DK130190, R01DK106419, R01DK121378, R01DK124318, P30DK120515, K23DK119460), NHLBI (P01HL147835), and NIAAA (U01AA029019); Academy of Finland grant 309263; the Novo Nordisk, EVO, and Sigrid Jusélius Foundations; and the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement 777377. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the EFPIA.