SOMAscan Proteomics Identifies Serum Biomarkers Associated With Liver Fibrosis in Patients With NASH

The Proteomics of MASLD Progression: Insights From Functional Analysis to Drive the Development of New Therapeutic Solutions

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading chronic liver disease worldwide, with alarming prevalence reaching epidemic proportions.

AIMS AND METHODS

The objective of this study is to provide a comprehensive review of the latest blood proteomics studies on MASLD and metabolic dysfunction-associated steatohepatitis (MASH), with emphasis on fibrosis. Furthermore, our objective is to conduct an analysis of protein pathways and interactions by integrating proteomics data using functional enrichment analysis of the deregulated proteins.

RESULTS

Notwithstanding the considerable discrepancies in the methodology and the number of proteins examined in the circulation, the analysis reveals a consistent pattern among the list of proteins that are decreased or increased in the blood of the affected patients. The relevant biological processes (BP) associated with down- and upregulated proteins are high-density lipoprotein remodelling and complement activation, respectively. The protein families identified include not only those expected to be involved in the immune system and cell adhesion and migration but also ligands of glycoproteins expressed in cells that have been subjected to stress and proteins containing the Sushi domain.

CONCLUSIONS

The application of cutting-edge methodologies to investigate the blood proteome in MASH is yielding insights that facilitate the elucidation of disease mechanisms and the identification of optimal noninvasive biomarkers. However, several challenges remain to be addressed in future research, including the generalisation of results on a global scale, the optimisation of analytical technologies and the implementation of large longitudinal studies to gain insights into the molecular mechanisms that underpin the development of advanced disease.

Artificial intelligence applied to 'omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment

Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.

Serum proteome signatures associated with liver steatosis in adolescents with obesity

PURPOSE

Childhood obesity, a pressing global health issue, significantly increases the risk of metabolic complications, including metabolic dysfunction associated with steatotic liver disease (MASLD). Accurate non-invasive tests for early detection and screening of steatosis are crucial. In this study, we explored the serum proteome, identifying proteins as potential biomarkers for inclusion in non-invasive steatosis diagnosis tests.

METHODS

Fifty-nine obese adolescents underwent ultrasonography to assess steatosis. Serum samples were collected and analyzed by targeted proteomics with the Proximity Extension Assay technology. Clinical and biochemical parameters were evaluated, and correlations among them, the individuated markers, and steatosis were performed. Receiver operating characteristic (ROC) curves were used to determine the steatosis diagnostic performance of the identified candidates, the fatty liver index (FLI), and their combination in a logistic regression model.

RESULTS

Significant differences were observed between subjects with and without steatosis in various clinical and biochemical parameters. Gender-related differences in the serum proteome were also noted. Five circulating proteins, including Cathepsin O (CTSO), Cadherin 2 (CDH2), and Prolyl endopeptidase (FAP), were identified as biomarkers for steatosis. CDH2, CTSO, Leukocyte Immunoglobulin Like Receptor A5 (LILRA5), BMI, waist circumference, HOMA-IR, and FLI, among others, significantly correlated with the steatosis degree. CDH2, FAP, and LDL combined in a logit model achieved a diagnostic performance with an AUC of 0.91 (95% CI 0.75-0.97, 100% sensitivity, 84% specificity).

CONCLUSIONS

CDH2 and FAP combined with other clinical parameters, represent useful tools for accurate diagnosis of fatty liver, emphasizing the importance of integrating novel markers into diagnostic algorithms for MASLD.

A prognostic molecular signature of hepatic steatosis is spatially heterogeneous and dynamic in human liver

Hepatic steatosis is a central phenotype in multi-system metabolic dysfunction and is increasing in parallel with the obesity pandemic. We use a translational approach integrating clinical phenotyping and outcomes, circulating proteomics, and tissue transcriptomics to identify dynamic, functional biomarkers of hepatic steatosis. Using multi-modality imaging and broad proteomic profiling, we identify proteins implicated in the progression of hepatic steatosis that are largely encoded by genes enriched at the transcriptional level in the human liver. These transcripts are differentially expressed across areas of steatosis in spatial transcriptomics, and several are dynamic during stages of steatosis. Circulating multi-protein signatures of steatosis strongly associate with fatty liver disease and multi-system metabolic outcomes. Using a humanized "liver-on-a-chip" model, we induce hepatic steatosis, confirming cell-specific expression of prioritized targets. These results underscore the utility of this approach to identify a prognostic, functional, dynamic "liquid biopsy" of human liver, relevant to biomarker discovery and mechanistic research applications.

Serum protein risk stratification score for diagnostic evaluation of metabolic dysfunction-associated steatohepatitis

BACKGROUND

Reliable, noninvasive tools to diagnose at-risk metabolic dysfunction-associated steatohepatitis (MASH) are urgently needed to improve management. We developed a risk stratification score incorporating proteomics-derived serum markers with clinical variables to identify high-risk patients with MASH (NAFLD activity score >4 and fibrosis score >2).

METHODS

In this 3-phase proteomic study of biopsy-proven metabolic dysfunction-associated steatotic fatty liver disease, we first developed a multi-protein predictor for discriminating NAFLD activity score >4 based on SOMAscan proteomics quantifying 1305 serum proteins from 57 US patients. Four key predictor proteins were verified by ELISA in the expanded US cohort (N = 168) and enhanced by adding clinical variables to create the 9-feature MASH Dx score, which predicted MASH and also high-risk MASH (F2+). The MASH Dx score was validated in 2 independent, external cohorts from Germany (N = 139) and Brazil (N = 177).

RESULTS

The discovery phase identified a 6-protein classifier that achieved an AUC of 0.93 for identifying MASH. Significant elevation of 4 proteins (THBS2, GDF15, SELE, and IGFBP7) was verified by ELISA in the expanded discovery and independently in the 2 external cohorts. MASH Dx score incorporated these proteins with established MASH risk factors (age, body mass index, ALT, diabetes, and hypertension) to achieve good discrimination between MASH and metabolic dysfunction-associated steatotic fatty liver disease without MASH (AUC: 0.87-discovery; 0.83-pooled external validation cohorts), with similar performance when evaluating high-risk MASH F2-4 (vs. MASH F0-1 and metabolic dysfunction-associated steatotic fatty liver disease without MASH).

CONCLUSIONS

The MASH Dx score offers the first reliable noninvasive approach combining novel, biologically plausible ELISA-based fibrosis markers and clinical parameters to detect high-risk MASH in patient cohorts from the United States, Brazil, and Europe.

Diagnostics and omics technologies for the detection and prediction of metabolic dysfunction-associated steatotic liver disease-related malignancies

The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, making it the leading etiology of chronic liver diseases and a prime cause of liver-related mortality. MASLD can progress into steatohepatitis (termed MASH), fibrosis, cirrhosis, and ultimately cancer. MASLD is associated with increased risks of hepatocellular carcinoma (HCC) and also extrahepatic malignancies, which can develop in both cirrhotic and non-cirrhotic patients, emphasizing the importance of identifying patients with MASLD at risk of developing MASLD-associated malignancies. However, the optimal screening, diagnostic, and risk stratification strategies for patients with MASLD at risk of cancer are still under debate. Individuals with MASH-associated cirrhosis are recommended to undergo surveillance for HCC (e.g. by ultrasound and biomarkers) every six months. No specific screening approaches for MASLD-related malignancies in non-cirrhotic cases are established to date. The rapidly developing omics technologies, including genetics, metabolomics, and proteomics, show great potential for discovering non-invasive markers to fulfill this unmet need. This review provides an overview on the incidence and mortality of MASLD-associated malignancies, current strategies for HCC screening, surveillance and diagnosis in patients with MASLD, and the evolving role of omics technologies in the discovery of non-invasive markers for the prediction and risk stratification of MASLD-associated HCC.

3D clustering of gene expression data from systemic autoinflammatory diseases using self-organizing maps (Clust3D)

Background and objective

Systemic autoinflammatory diseases (SAIDs) are characterized by widespread inflammation, but for most of them there is a lack of specific biomarkers for accurate diagnosis. Although a number of machine learning algorithms have been used to analyze SAID datasets, aiding in the discovery of novel biomarkers, there is a growing recognition of the importance of SAID timeseries clustering, as it can capture the temporal dynamics of gene expression patterns.

Methodology

This paper proposes a novel clustering methodology to efficiently associate three-dimensional data. The algorithm utilizes competitive learning to create a self-organizing neural network and adjust neuron positions in time-dependent and high dimensional feature space in order to assign them as clustering centers. The quantitative evaluation of the clustering was based on well-known clustering indices. Furthermore, a differential expression analysis and classification pipeline was employed to assess the capability of the proposed methodology to extract more accurate pathway-specific genes from its clusters. For that, a comparative analysis was also conducted against a heuristic timeseries clustering method.

Results

The proposed methodology achieved better overall clustering indices scores and classification metrics using genes derived from its clusters. Notable cases include a threefold increase in the Calinski-Harabasz clustering index, a twofold improvement in the Davies-Bouldin clustering index and a ∼ 60 % increase in the classification specificity score.

Conclusion

A novel clustering methodology was developed and applied on several gene expression timeseries datasets from systemic autoinflammatory diseases, and its ability to efficiently produce well separated clusters compared to existing heuristic methods was demonstrated.

Advances in omics data for eosinophilic esophagitis: moving towards multi-omics analyses

Eosinophilic esophagitis (EoE) is a chronic, allergic inflammatory disease of the esophagus characterized by eosinophil accumulation and has a growing global prevalence. EoE significantly impairs quality of life and poses a substantial burden on healthcare resources. Currently, only two FDA-approved medications exist for EoE, highlighting the need for broader research into its management and prevention. Recent advancements in omics technologies, such as genomics, epigenetics, transcriptomics, proteomics, and others, offer new insights into the genetic and immunologic mechanisms underlying EoE. Genomic studies have identified genetic loci and mutations associated with EoE, revealing predispositions that vary by ancestry and indicating EoE's complex genetic basis. Epigenetic studies have uncovered changes in DNA methylation and chromatin structure that affect gene expression, influencing EoE pathology. Transcriptomic analyses have revealed a distinct gene expression profile in EoE, dominated by genes involved in activated type 2 immunity and epithelial barrier function. Proteomic approaches have furthered the understanding of EoE mechanisms, identifying potential new biomarkers and therapeutic targets. However, challenges in integrating diverse omics data persist, largely due to their complexity and the need for advanced computational methods. Machine learning is emerging as a valuable tool for analyzing extensive and intricate datasets, potentially revealing new aspects of EoE pathogenesis. The integration of multi-omics data through sophisticated computational approaches promises significant advancements in our understanding of EoE, improving diagnostics, and enhancing treatment effectiveness. This review synthesizes current omics research and explores future directions for comprehensively understanding the disease mechanisms in EoE.

Prediagnostic plasma proteomics profile for hepatocellular carcinoma

OBJECTIVE

Proteomics may discover pathophysiological changes related to hepatocellular carcinoma, an aggressive and lethal type of cancer with low sensitivity for early stage diagnosis.

DESIGN

We measured 1305 prediagnostic (median = 12.7 years) SomaScan proteins from 54 pairs of healthy individuals who subsequently developed hepatocellular carcinoma and matched non-hepatocellular carcinoma control individuals from the Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS). Candidate proteins were validated in the independent, prospective UK Biobank Pharma Proteomics Project (UKB-PPP).

RESULTS

In NHS and HPFS, we identified 56 elevated proteins in hepatocellular carcinoma with an absolute fold change of more than 1.2 and a Wald test P value less than .05 in conditional logistic regression analysis. Ingenuity pathway analysis identified enrichment of pathways associated with cell viability, adhesion, proteolysis, apoptosis, and inflammatory response. Four proteins-chitinase-3-like protein 1, growth differentiation factor 15, interleukin-1 receptor antagonist protein, and E-selectin-showed strong positive associations with hepatocellular carcinoma and were thus validated by enzyme-linked immunosorbent assay (odds ratio = 2.48-14.7, all P < .05) in the NHS and HPFS and by Olink platform (hazard ratio = 1.90-3.93, all P < .05) in the UKB-PPP. Adding these 4 proteins to a logistic regression model of traditional hepatocellular carcinoma risk factors increased the area under the curve from 0.67 to 0.87 in the NHS and HPFS. Consistently, model area under the curve was 0.88 for hepatocellular carcinoma risk prediction in the UKB-PPP.

CONCLUSION

However, the limited number of hepatocellular carcinoma patients in the cohorts necessitates caution in interpreting our findings, emphasizing the need for further validation in high-risk populations.

Opportunities and barriers in omics-based biomarker discovery for steatotic liver diseases

The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognostication and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to apply technological advances to clinical needs, for instance in the development of precision biomarkers for personalised medicine. Via omics technologies, biological processes from the genes to circulating protein, as well as the microbiome - including bacteria, viruses and fungi, can be investigated on an axis. However, there are important barriers to omics-based biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate them across diverse populations presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression at different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated has enabled the hypothesis-free discovery of a plethora of candidate biomarkers that warrant further validation. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.

Are we ready for genetic testing in metabolic dysfunction-associated steatotic liver disease?

Metabolic dysfunction-associated steatotic liver disease (MASLD), with its steadily increasing prevalence, represents now a major problem in public health. A proper referral could benefit from tools allowing more precise risk stratification. To this end, in recent decades, several genetic variants that may help predict and refine the risk of development and progression of MASLD have been investigated. In this review, we aim to discuss the role genetics in MASLD plays in everyday clinical practice. We performed a comprehensive literature search of PubMed for relevant publications. Available evidence highlights the emergence of genetic-based noninvasive algorithms for diagnosing fatty liver, metabolic dysfunction-associated steatohepatitis, fibrosis progression and occurrence of liver-related outcomes including hepatocellular carcinoma. Nevertheless, their accuracy is not optimal and application in everyday clinical practice remains challenging. Furthermore, susceptible genetic markers have recently become subjects of great scientific interest as therapeutic targets in precision medicine. In conclusion, decisional algorithms based on genetic testing in MASLD to facilitate the clinician decisions on management and treatment are under growing investigation and could benefit from artificial intelligence methodology.

Plasma ALS and Gal-3BP differentiate early from advanced liver fibrosis in MASLD patients

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is estimated to affect 30% of the world's population, and its prevalence is increasing in line with obesity. Liver fibrosis is closely related to mortality, making it the most important clinical parameter for MASLD. It is currently assessed by liver biopsy - an invasive procedure that has some limitations. There is thus an urgent need for a reliable non-invasive means to diagnose earlier MASLD stages.

METHODS

A discovery study was performed on 158 plasma samples from histologically-characterised MASLD patients using mass spectrometry (MS)-based quantitative proteomics. Differentially abundant proteins were selected for verification by ELISA in the same cohort. They were subsequently validated in an independent MASLD cohort (n = 200).

RESULTS

From the 72 proteins differentially abundant between patients with early (F0-2) and advanced fibrosis (F3-4), we selected Insulin-like growth factor-binding protein complex acid labile subunit (ALS) and Galectin-3-binding protein (Gal-3BP) for further study. In our validation cohort, AUROCs with 95% CIs of 0.744 [0.673 - 0.816] and 0.735 [0.661 - 0.81] were obtained for ALS and Gal-3BP, respectively. Combining ALS and Gal-3BP improved the assessment of advanced liver fibrosis, giving an AUROC of 0.796 [0.731. 0.862]. The {ALS; Gal-3BP} model surpassed classic fibrosis panels in predicting advanced liver fibrosis.

CONCLUSIONS

Further investigations with complementary cohorts will be needed to confirm the usefulness of ALS and Gal-3BP individually and in combination with other biomarkers for diagnosis of liver fibrosis. With the availability of ELISA assays, these findings could be rapidly clinically translated, providing direct benefits for patients.

Non-invasive diagnosis of non-alcoholic fatty liver disease: Current status and future perspective

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease throughout the world. Hepatocellular carcinoma (HCC) and liver cirrhosis can result from nonalcoholic steatohepatitis (NASH), the severe stage of NAFLD progression. By some estimates, NAFLD affects almost one-third of the world's population, which is completely new and serious public health issue. Unfortunately, NAFLD is diagnosed by exclusion, and the gold standard for identifying NAFLD/NASH and reliably measuring liver fibrosis remains liver biopsy, which is an invasive, costly, time-consuming procedure and involves variable inter-observer diagnosis. With the progress of omics and imaging techniques, numerous non-invasive serological assays have been generated and developed. On the basis of these developments, non-invasive biomarkers and imaging techniques have been combined to increase diagnostic accuracy. This review provides information for the diagnosis and assessment of NAFLD/NASH in clinical practice going forward and may assist the clinician in making an early and accurate diagnosis and in proposing a cost-effective patient surveillance. We discuss newly identified and validated non-invasive diagnostic methods from biopsy-confirmed NAFLD patient studies and their implementation in clinical practice, encompassing NAFLD/NASH diagnosis and differentiation, fibrosis assessment, and disease progression monitoring. A series of tests, including 20-carboxy arachidonic acid (20-COOH AA) and 13,14-dihydro-15-keto prostaglandin D2 (dhk PGD2), were found to be potentially the most accurate non-invasive tests for diagnosing NAFLD. Additionally, the Three-dimensional magnetic resonance imaging (3D-MRE), combination of the FM-fibro index and Liver stiffness measurement (FM-fibro LSM index) and the machine learning algorithm (MLA) tests are more accurate than other tests in assessing liver fibrosis. However, it is essential to use bigger cohort studies to corroborate a number of non-invasive diagnostic tests with extremely elevated diagnostic values.

A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It, therefore, represents both a global public health threat and a precision medicine challenge. Artificial intelligence (AI) is being investigated in MASLD to develop reproducible, quantitative, and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD. This review details the different applications of AI and machine learning algorithms in MASLD, particularly in analyzing electronic health record, digital pathology, and imaging data. Additionally, it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field. Using a new national-level 'data commons' (SteatoSITE) as an exemplar, the opportunities, as well as the technical challenges of large-scale databases in MASLD research, are highlighted.

Human APOE4 Protects High-Fat and High-Sucrose Diet Fed Targeted Replacement Mice against Fatty Liver Disease Compared to APOE3

Recent genome- and exome-wide association studies suggest that the human APOE ε4 allele protects against non-alcoholic fatty liver disease (NAFLD), while ε3 promotes hepatic steatosis and steatohepatitis. The present study aimed at examining the APOE genotype-dependent development of fatty liver disease and its underlying mechanisms in a targeted replacement mouse model. Male mice expressing the human APOE3 or APOE4 protein isoforms on a C57BL/6J background and unmodified C57BL/6J mice were chronically fed a high-fat and high-sucrose diet to induce obesity. After 7 months, body weight gain was more pronounced in human APOE than endogenous APOE expressing mice with elevated plasma biomarkers suggesting aggravated metabolic dysfunction. APOE3 mice exhibited the highest liver weights and, compared to APOE4, massive hepatic steatosis. An untargeted quantitative proteome analysis of the liver identified a high number of proteins differentially abundant in APOE3 versus APOE4 mice. The majority of the higher abundant proteins in APOE3 mice could be grouped to inflammation and damage-associated response, and lipid storage, amongst others. Results of the targeted qRT-PCR and Western blot analyses contribute to the overall finding that APOE3 as opposed to APOE4 promotes hepatic steatosis, inflammatory- and damage-associated response signaling and fibrosis in the liver of obese mice. Our experimental data substantiate the observation of an increased NAFLD-risk associated with the human APOEε3 allele, while APOEε4 appears protective. The underlying mechanisms of the protection possibly involve a higher capacity of nonectopic lipid deposition in subcutaneous adipose tissue and lower hepatic pathogen recognition in the APOE4 mice.

A prognostic molecular signature of hepatic steatosis is spatially heterogeneous and dynamic in human liver

Metabolic dysfunction-associated steatotic liver disease (MASLD) prevalence is increasing in parallel with an obesity pandemic, calling for novel strategies for prevention and treatment. We defined a circulating proteome of human MASLD across ≈7000 proteins in ≈5000 individuals from diverse, at-risk populations across the metabolic health spectrum, demonstrating reproducible diagnostic performance and specifying both known and novel metabolic pathways relevant to MASLD (central carbon and amino acid metabolism, hepatocyte regeneration, inflammation, fibrosis, insulin sensitivity). A parsimonious proteomic signature of MASLD was associated with a protection from MASLD and its related multi-system metabolic consequences in >26000 free-living individuals, with an additive effect to polygenic risk. The MASLD proteome was encoded by genes that demonstrated transcriptional enrichment in liver, with spatial transcriptional activity in areas of steatosis in human liver biopsy and dynamicity for select targets in human liver across stages of steatosis. We replicated several top relations from proteomics and spatial tissue transcriptomics in a humanized "liver-on-a-chip" model of MASLD, highlighting the power of a full translational approach to discovery in MASLD. Collectively, these results underscore utility of blood-based proteomics as a dynamic "liquid biopsy" of human liver relevant to clinical biomarker and mechanistic applications.

Applying proteomics in metabolic dysfunction-associated steatotic liver disease: From mechanism to biomarkers

Metabolic dysfunction-associated steatotic liver disease (MASLD), which represents the most common cause of liver disease, is emerging as a major health problem around the world. However, the molecular events that underline the pathogenesis and the progression of MASLD remain to be fully elucidated. Advanced stages of MASLD is strongly associated with liver-related outcomes and overall mortality. Despite this, highly accurate, sensitive, and non-invasive diagnostic tools are currently not aviailable, yet no FDA approved drugs for MASLD. The advance of proteomics has enable the study of protein expression, post-translational modifications (PTMs), subcellular distribution, and interactions. In this review, we discuss insights gained from the recent proteomics studies that shed new light on the pathogenesis, diagnosis and potential theraputic targets of MASLD.

Proteome profiling identifies circulating biomarkers associated with hepatic steatosis in subjects with Prader-Willi syndrome

Introduction

Prader-Willi syndrome (PWS) is a rare genetic disorder characterized by loss of expression of paternal chromosome 15q11.2-q13 genes. Individuals with PWS exhibit unique physical, endocrine, and metabolic traits associated with severe obesity. Identifying liver steatosis in PWS is challenging, despite its lower prevalence compared to non-syndromic obesity. Reliable biomarkers are crucial for the early detection and management of this condition associated with the complex metabolic profile and cardiovascular risks in PWS.

Methods

Circulating proteome profiling was conducted in 29 individuals with PWS (15 with steatosis, 14 without) using the Olink Target 96 metabolism and cardiometabolic panels. Correlation analysis was performed to identify the association between protein biomarkes and clinical variables, while the gene enrichment analysis was conducted to identify pathways linked to deregulated proteins. Receiver operating characteristic (ROC) curves assessed the discriminatory power of circulating protein while a logistic regression model evaluated the potential of a combination of protein biomarkers.

Results

CDH2, CTSO, QDPR, CANT1, ALDH1A1, TYMP, ADGRE, KYAT1, MCFD, SEMA3F, THOP1, TXND5, SSC4D, FBP1, and CES1 exhibited a significant differential expression in liver steatosis, with a progressive increase from grade 1 to grade 3. FBP1, CES1, and QDPR showed predominant liver expression. The logistic regression model, -34.19 + 0.85 * QDPR*QDPR + 0.75 * CANT1*TYMP - 0.46 * THOP1*ALDH1A, achieved an AUC of 0.93 (95% CI: 0.63-0.99), with a sensitivity of 93% and specificity of 80% for detecting steatosis in individuals with PWS. These biomarkers showed strong correlations among themselves and were involved in an interconnected network of 62 nodes, related to seven metabolic pathways. They were also significantly associated with cholesterol, LDL, triglycerides, transaminases, HbA1c, FLI, APRI, and HOMA, and showed a negative correlation with HDL levels.

Conclusion

The biomarkers identified in this study offer the potential for improved patient stratification and personalized therapeutic protocols.

Carotenoids in Health as Studied by Omics-Related Endpoints

Carotenoids have been associated with risk reduction for several chronic diseases, including the association of their dietary intake/circulating levels with reduced incidence of obesity, type 2 diabetes, certain types of cancer, and even lower total mortality. In addition to some carotenoids constituting vitamin A precursors, they are implicated in potential antioxidant effects and pathways related to inflammation and oxidative stress, including transcription factors such as nuclear factor κB and nuclear factor erythroid 2-related factor 2. Carotenoids and metabolites may also interact with nuclear receptors, mainly retinoic acid receptor/retinoid X receptor and peroxisome proliferator-activated receptors, which play a role in the immune system and cellular differentiation. Therefore, a large number of downstream targets are likely influenced by carotenoids, including but not limited to genes and proteins implicated in oxidative stress and inflammation, antioxidation, and cellular differentiation processes. Furthermore, recent studies also propose an association between carotenoid intake and gut microbiota. While all these endpoints could be individually assessed, a more complete/integrative way to determine a multitude of health-related aspects of carotenoids includes (multi)omics-related techniques, especially transcriptomics, proteomics, lipidomics, and metabolomics, as well as metagenomics, measured in a variety of biospecimens including plasma, urine, stool, white blood cells, or other tissue cellular extracts. In this review, we highlight the use of omics technologies to assess health-related effects of carotenoids in mammalian organisms and models.

Advances in Noninvasive Biomarkers for Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) currently represents one of the most common liver diseases worldwide. Early diagnosis and disease staging is crucial, since it is mainly asymptomatic, but can progress to nonalcoholic steatohepatitis (NASH) or cirrhosis or even lead to the development of hepatocellular carcinoma. Over time, efforts have been put into developing noninvasive diagnostic and staging methods in order to replace the use of a liver biopsy. The noninvasive methods used include imaging techniques that measure liver stiffness and biological markers, with a focus on serum biomarkers. Due to the impressive complexity of the NAFLD's pathophysiology, biomarkers are able to assay different processes involved, such as apoptosis, fibrogenesis, and inflammation, or even address the genetic background and "omics" technologies. This article reviews not only the currently validated noninvasive methods to investigate NAFLD but also the promising results regarding recently discovered biomarkers, including biomarker panels and the combination of the currently validated evaluation methods and serum markers.

Found in translation-Fibrosis in metabolic dysfunction-associated steatohepatitis (MASH)

Metabolic dysfunction-associated steatohepatitis (MASH) is a severe form of liver disease that poses a global health threat because of its potential to progress to advanced fibrosis, leading to cirrhosis and liver cancer. Recent advances in single-cell methodologies, refined disease models, and genetic and epigenetic insights have provided a nuanced understanding of MASH fibrogenesis, with substantial cellular heterogeneity in MASH livers providing potentially targetable cell-cell interactions and behavior. Unlike fibrogenesis, mechanisms underlying fibrosis regression in MASH are still inadequately understood, although antifibrotic targets have been recently identified. A refined antifibrotic treatment framework could lead to noninvasive assessment and targeted therapies that preserve hepatocellular function and restore the liver's architectural integrity.

Classification of Inflammation of Unknown Origin patients based on RNA-seq and SomaScan data

A preliminary analysis was conducted on data acquired from RNA sequencing and SomaScan platforms, for the classification of patients with Inflammation of Unknown Origin. To this end, a multimodal data integration approach was designed, by combining the two platforms, in order to assess the potentiality of learning estimators, using the differentially expressed features from the independent profiling experiments of both platforms. The classification framing was the differentiation of Inflammation of Unknown Origin patients against a multitude of Systemic Autoinflammatory disease patients. Separate false discovery rate analyses were performed on each dataset to extract statistically significant features between the two designated sample groups. Genomic analysis managed higher overall classification metrics compared to proteomic analysis, averaging an ~19% increase overall metrics and classifiers, with a ~0.07% increase in standard error. The multimodal data integration approach achieved similar results to the individual platforms' analyses. More specifically, it managed the same classification accuracy, sensitivity, and specificity scores as the best individual analysis, with the simple Logistic Regression estimator.Clinical Relevance- This study highlights the advantage of exploiting RNA sequencing data to identify potential Inflammation of Unknown Origin disease specific biomarkers, even against other Systemic Autoinflammatory diseases. These findings are further emphasized given the non-apparent clinical discrepancy between Inflammation of Unknown Origin and other Systemic Autoinflammatory diseases.

Novel proteomic technologies to address gaps in pre-clinical ovarian cancer biomarker discovery efforts

INTRODUCTION

An estimated 20,000 women in the United States will receive a diagnosis of ovarian cancer in 2023. Late-stage diagnosis is associated with poor prognosis. There is a need for novel diagnostic biomarkers for ovarian cancer to improve early-stage detection and novel prognostic biomarkers to improve patient treatment.

AREAS COVERED

This review provides an overview of the clinicopathological features of ovarian cancer and the currently available biomarkers and treatment options. Two affinity-based platforms using proximity extension assays (Olink) and DNA aptamers (SomaLogic) are described in the context of highly reproducible and sensitive multiplexed assays for biomarker discovery. Recent developments in ion mobility spectrometry are presented as novel techniques to apply to the biomarker discovery pipeline. Examples are provided of how these aforementioned methods are being applied to biomarker discovery efforts in various diseases, including ovarian cancer.

EXPERT OPINION

Translating novel ovarian cancer biomarkers from candidates in the discovery phase to bona fide biomarkers with regulatory approval will have significant benefits for patients. Multiplexed affinity-based assay platforms and novel mass spectrometry methods are capable of quantifying low abundance proteins to aid biomarker discovery efforts by enabling the robust analytical interrogation of the ovarian cancer proteome.

Noninvasive Assessment of Liver Fibrosis in NAFLD

Nonalcoholic fatty liver disease (NAFLD) has emerged as a leading cause of liver-related morbidity and mortality worldwide, afflicting approximately a billion individuals. NAFLD is a slowly progressive disease that may evolve in a subset of patients toward cirrhosis, hepatocellular carcinoma, and end-stage liver disease. Liver fibrosis severity is the strongest predictor of clinical outcomes. The emergence of effective therapeutics on the horizon highlights the need to identify among patients with NAFLD, those with severe fibrosis or cirrhosis, who are the most at risk of developing complications and target them for therapy. Liver biopsy has been the reference standard for this purpose. However, it is not suitable for large-scale population evaluation, given its well-known limitations (invasiveness, rare but severe complications, and sampling variability). Thus, there have been major efforts to develop simple noninvasive tools that can be used in routine clinical settings and in drug development. Noninvasive approaches are based on the quantification of biomarkers in serum samples or on the measurement of liver stiffness, using either ultrasound- or magnetic resonance-based elastography techniques. This review provides a roadmap for future development and integration of noninvasive tools in clinical practice and in drug development in NAFLD. We discuss herein the principles for their development and validation, their use in clinical practice, including for diagnosis of NAFLD, risk stratification in primary care and hepatology settings, prediction of long-term liver-related and non-liver-related outcomes, monitoring of fibrosis progression and regression, and response to future treatment.

Plasma proteomic signature of fatty liver disease: The Rotterdam Study

BACKGROUND AND AIMS

Fatty liver disease (FLD) is caused by excess fat in the liver, and its global prevalence exceeds 33%. The role of protein expression on the pathogenesis of FLD and accompanied fibrosis and its potential as a disease biomarker is currently not clear. Hence, we aimed to identify plasma proteomics associated with FLD and fibrosis using population-based data.

APPROACH AND RESULTS

Blood samples were collected from 2578 participants from the population-based Rotterdam Study cohort. The proximity extension assay reliably measured plasma levels of 171 cardiometabolic and inflammatory-related proteins (Olink Proteomics). FLD was assessed by ultrasound, and fibrosis by transient elastography. Logistic regression models quantified the association of plasma proteomics with FLD and fibrosis. In addition, we aimed to validate our results in liver organoids. The cross-sectional analysis identified 27 proteins significantly associated with FLD surpassing the Bonferroni-corrected p <2.92×10 -4 . The strongest association was observed for FGF-21 (β=0.45, p =1.07×10 -18 ) and carboxylesterase 1 (CES1) protein (β=0.66, p =4.91×10 -40 ). Importantly, 15 of the 27 proteins significantly associated with FLD were also associated with liver fibrosis. Finally, consistent with plasma proteomic profiling, we found the expression levels of IL-18 receptor 1 (IL-18R1) and CES1 to be upregulated in an FLD model of 3-dimensional culture human liver organoids.

CONCLUSIONS

Among the general population, several inflammatory and cardiometabolic plasma proteins were associated with FLD and fibrosis. Particularly, plasma levels of FGF-21, IL-18R1, and CES1 were largely dependent on the presence of FLD and fibrosis and may therefore be important in their pathogenesis.

Enhanced Multiplexing Technology for Proteomics

The identification of thousands of proteins and their relative levels of expression has furthered understanding of biological processes and disease and stimulated new systems biology hypotheses. Quantitative proteomics workflows that rely on analytical assays such as mass spectrometry have facilitated high-throughput measurements of proteins partially due to multiplexing. Multiplexing allows proteome differences across multiple samples to be measured simultaneously, resulting in more accurate quantitation, increased statistical robustness, reduced analysis times, and lower experimental costs. The number of samples that can be multiplexed has evolved from as few as two to more than 50, with studies involving more than 10 samples being denoted as enhanced multiplexing or hyperplexing. In this review, we give an update on emerging multiplexing proteomics techniques and highlight advantages and limitations for enhanced multiplexing strategies.

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

The liver is a multifaceted organ that is responsible for many critical functions encompassing amino acid, carbohydrate, and lipid metabolism, all of which make a healthy liver essential for the human body. Contemporary imaging methodologies have remarkable diagnostic accuracy in discerning focal liver lesions; however, a comprehensive understanding of diffuse liver diseases is a requisite for radiologists to accurately diagnose or predict the progression of such lesions within clinical contexts. Nonetheless, the conventional attributes of radiological features, including morphology, size, margin, density, signal intensity, and echoes, limit their clinical utility. Radiomics is a widely used approach that is characterized by the extraction of copious image features from radiographic depictions, which gives it considerable potential in addressing this limitation. It is worth noting that functional or molecular alterations occur significantly prior to the morphological shifts discernible by imaging modalities. Consequently, the explication of potential mechanisms by multiomics analyses (encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics) is essential for investigating putative signal pathway regulations from a radiological viewpoint. In this review, we elaborate on the principal pathological categorizations of diffuse liver diseases, the evaluation of multiomics approaches pertaining to diffuse liver diseases, and the prospective value of predictive models. Accordingly, the overarching objective of this review is to scrutinize the interrelations between radiological features and bioinformatics as well as to consider the development of prediction models predicated on radiobioinformatics as integral components of clinical decision support systems for diffuse liver diseases.

Multidimensional landscape of non-alcoholic fatty liver disease-related disease spectrum uncovered by big omics data: Profiling evidence and new perspectives

Characterized by hepatic lipid accumulation, non-alcoholic fatty liver disease (NAFLD) is a multifactorial metabolic disorder that could promote the progression of non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC). Benefiting from recent advances in omics technologies, such as high-throughput sequencing, voluminous profiling data in HCC-integrated molecular science into clinical medicine helped clinicians with rational guidance for treatments. In this review, we conclude the majority of publicly available omics data on the NAFLD-related disease spectrum and bring up new insights to inspire next-generation therapeutics against this increasingly prevalent disease spectrum in the post-genomic era.

A proteo-transcriptomic map of non-alcoholic fatty liver disease signatures

Non-alcoholic fatty liver disease (NAFLD) is a common, progressive liver disease strongly associated with the metabolic syndrome. It is unclear how progression of NAFLD towards cirrhosis translates into systematic changes in circulating proteins. Here, we provide a detailed proteo-transcriptomic map of steatohepatitis and fibrosis during progressive NAFLD. In this multicentre proteomic study, we characterize 4,730 circulating proteins in 306 patients with histologically characterized NAFLD and integrate this with transcriptomic analysis in paired liver tissue. We identify circulating proteomic signatures for active steatohepatitis and advanced fibrosis, and correlate these with hepatic transcriptomics to develop a proteo-transcriptomic signature of 31 markers. Deconvolution of this signature by single-cell RNA sequencing reveals the hepatic cell types likely to contribute to proteomic changes with disease progression. As an exemplar of use as a non-invasive diagnostic, logistic regression establishes a composite model comprising four proteins (ADAMTSL2, AKR1B10, CFHR4 and TREM2), body mass index and type 2 diabetes mellitus status, to identify at-risk steatohepatitis.

Defining the serum proteomic signature of hepatic steatosis, inflammation, ballooning and fibrosis in non-alcoholic fatty liver disease

BACKGROUND & AIMS

Despite recent progress, non-invasive tests for the diagnostic assessment and monitoring of non-alcoholic fatty liver disease (NAFLD) remain an unmet need. Herein, we aimed to identify diagnostic signatures of the key histological features of NAFLD.

METHODS

Using modified-aptamer proteomics, we assayed 5,220 proteins in each of 2,852 single serum samples from 636 individuals with histologically confirmed NAFLD. We developed and validated dichotomized protein-phenotype models to identify clinically relevant severities of steatosis (grade 0 vs. 1-3), hepatocellular ballooning (0 vs. 1 or 2), lobular inflammation (0-1 vs. 2-3) and fibrosis (stages 0-1 vs. 2-4).

RESULTS

The AUCs of the four protein models, based on 37 analytes (18 not previously linked to NAFLD), for the diagnosis of their respective components (at a clinically relevant severity) in training/paired validation sets were: fibrosis (AUC 0.92/0.85); steatosis (AUC 0.95/0.79), inflammation (AUC 0.83/0.72), and ballooning (AUC 0.87/0.83). An additional outcome, at-risk NASH, defined as steatohepatitis with NAFLD activity score ≥4 (with a score of at least 1 for each of its components) and fibrosis stage ≥2, was predicted by multiplying the outputs of each individual component model (AUC 0.93/0.85). We further evaluated their ability to detect change in histology following treatment with placebo, pioglitazone, vitamin E or obeticholic acid. Component model scores significantly improved in the active therapies vs. placebo, and differential effects of vitamin E, pioglitazone, and obeticholic acid were identified.

CONCLUSIONS

Serum protein scanning identified signatures corresponding to the key components of liver biopsy in NAFLD. The models developed were sufficiently sensitive to characterize the longitudinal change for three different drug interventions. These data support continued validation of these proteomic models to enable a "liquid biopsy"-based assessment of NAFLD.

CLINICAL TRIAL NUMBER

Not applicable.

IMPACT AND IMPLICATIONS

An aptamer-based protein scan of serum proteins was performed to identify diagnostic signatures of the key histological features of non-alcoholic fatty liver disease (NAFLD), for which no approved non-invasive diagnostic tools are currently available. We also identified specific protein signatures related to the presence and severity of NAFLD and its histological components that were also sensitive to change over time. These are fundamental initial steps in establishing a serum proteome-based diagnostic signature of NASH and provide the rationale for using these signatures to test treatment response and to identify several novel targets for evaluation in the pathogenesis of NAFLD.

Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction

This study investigates the ability of high-throughput aptamer-based platform to identify circulating biomarkers able to predict occurrence of heart failure (HF), in blood samples collected during hospitalization of patients suffering from a first myocardial infarction (MI). REVE-1 (derivation) and REVE-2 (validation) cohorts included respectively 254 and 238 patients, followed up respectively 9 · 2 ± 4 · 8 and 7 · 6 ± 3 · 0 years. A blood sample collected during hospitalization was used for quantifying 4,668 proteins. Fifty proteins were significantly associated with long-term occurrence of HF with all-cause death as the competing event. k-means, an unsupervised clustering method, identified two groups of patients based on expression levels of the 50 proteins. Group 2 was significantly associated with a higher risk of HF in both cohorts. These results showed that a subset of 50 selected proteins quantified during hospitalization of MI patients is able to stratify and predict the long-term occurrence of HF.

Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions

Rapid development of biotechnology has led to the generation of vast amounts of multi-omics data, necessitating the advancement of bioinformatics and artificial intelligence to enable computational modeling to diagnose and predict clinical outcome. Both conventional machine learning and new deep learning algorithms screen existing data unbiasedly to uncover patterns and create models that can be valuable in informing clinical decisions. We summarized published literature on the use of AI models trained on omics datasets, with and without clinical data, to diagnose, risk-stratify, and predict survivability of patients with non-malignant liver diseases. A total of 20 different models were tested in selected studies. Generally, the addition of omics data to regular clinical parameters or individual biomarkers improved the AI model performance. For instance, using NAFLD fibrosis score to distinguish F0-F2 from F3-F4 fibrotic stages, the area under the curve (AUC) was 0.87. When integrating metabolomic data by a GMLVQ model, the AUC drastically improved to 0.99. The use of RF on multi-omics and clinical data in another study to predict progression of NAFLD to NASH resulted in an AUC of 0.84, compared to 0.82 when using clinical data only. A comparison of RF, SVM and kNN models on genomics data to classify immune tolerant phase in chronic hepatitis B resulted in AUC of 0.8793-0.8838 compared to 0.6759-0.7276 when using various serum biomarkers. Overall, the integration of omics was shown to improve prediction performance compared to models built only on clinical parameters, indicating a potential use for personalized medicine in clinical setting.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Multiomics study of nonalcoholic fatty liver disease

Nonalcoholic fatty liver (NAFL) and its sequelae are growing health problems. We performed a genome-wide association study of NAFL, cirrhosis and hepatocellular carcinoma, and integrated the findings with expression and proteomic data. For NAFL, we utilized 9,491 clinical cases and proton density fat fraction extracted from 36,116 liver magnetic resonance images. We identified 18 sequence variants associated with NAFL and 4 with cirrhosis, and found rare, protective, predicted loss-of-function variants in MTARC1 and GPAM, underscoring them as potential drug targets. We leveraged messenger RNA expression, splicing and predicted coding effects to identify 16 putative causal genes, of which many are implicated in lipid metabolism. We analyzed levels of 4,907 plasma proteins in 35,559 Icelanders and 1,459 proteins in 47,151 UK Biobank participants, identifying multiple proteins involved in disease pathogenesis. We show that proteomics can discriminate between NAFL and cirrhosis. The present study provides insights into the development of noninvasive evaluation of NAFL and new therapeutic options.

The circulating proteomic signature of alcohol-associated liver disease

Despite being a leading cause of advanced liver disease, alcohol-associated liver disease (ALD) has no effective medical therapies. The circulating proteome, which comprises proteins secreted by different cells and tissues in the context of normal physiological function or in the setting of disease and illness, represents an attractive target for uncovering novel biology related to the pathogenesis of ALD. In this work, we used the aptamer-based SomaScan proteomics platform to quantify the relative concentration of over 1300 proteins in a well-characterized cohort of patients with the spectrum of ALD. We found a distinct circulating proteomic signature that correlated with ALD severity, including over 600 proteins that differed significantly between ALD stages, many of which have not previously been associated with ALD to our knowledge. Notably, certain proteins that were markedly dysregulated in patients with alcohol-associated hepatitis were also altered, to a lesser degree, in patients with subclinical ALD and may represent early biomarkers for disease progression. Taken together, our work highlights the vast and distinct changes in the circulating proteome across the wide spectrum of ALD, identifies potentially novel biomarkers and therapeutic targets, and provides a proteomic resource atlas for ALD researchers and clinicians.

Diagnostic and Prognostic Roles of Thrombospondin-2 in Digestive System Cancers

Background

Cancers of digestive system have high case-fatality rate. It is important to find more appropriate methods in diagnosing and predicting gastrointestinal malignances. And thrombospondin-2 (TSP-2) was reported to have the functions, although results were not identical. So we performed this meta-analysis to clarify the significance of TSP-2 in this area.

Methods

PubMed, Embase, Web of Science, Cochrane Library, and Clinicaltrial.gov were searched for relevant studies. Data were extracted from these involved records. For the meta-analysis of diagnostic test, bivariate mixed effect model was used to estimate diagnostic accuracy. For prognosis part, HRs and their 95% CIs were pooled to compare the overall survival (OS) and disease-free survival (DFS) between patients with high TSP-2 and low TSP-2.

Results

Nine records were eligible for the analysis of diagnostic test. Pooled results were as follows: sensitivity 0.60 (0.52, 0.68), specificity 0.96 (0.91, 0.98), positive likelihood ratio (PLR) 15.4 (7.3, 32.2), negative likelihood ratio (NLR) 0.42 (0.34, 0.50), and diagnostic odds ratio (DOR) 37 (18, 76). While in prognosis part, 10 articles were included. Patients with increased TSP-2 had shorter OS (HR = 1.64, 95% CI = 1.21-2.22); however, no difference was found in DFS between TSP-2 high and low groups (HR = 1.44, 95% CI = 0.28-7.33).

Conclusions

TSP-2, as a diagnostic marker, has a high specificity but a moderate sensitivity. Meanwhile, it plays a role in predicting OS. Therefore, making TSP-2 a routine assay could be beneficial to high-risk individuals and patients with digestive malignances.

Serum Thrombospondin-2 Levels Are Closely Associated With the Severity of Metabolic Syndrome and Metabolic Associated Fatty Liver Disease

CONTEXT

Metabolic associated fatty liver disease (MAFLD) is the hepatic manifestation of obesity-related metabolic syndrome (MetS). Noninvasive biomarkers for monitoring the progression and severity of these metabolic comorbidities are needed.

OBJECTIVES

To investigate the associations of serum thrombospondin-2 (TSP2) with MetS and MAFLD severity, and the potential diagnostic value of serum TSP2 for identifying at-risk metabolic associated steatohepatitis (MASH).

METHODS

Blood samples, clinical data, and liver biopsies were collected from consecutively recruited 252 individuals with morbid obesity receiving bariatric surgery. Histopathology samples of liver biopsies were examined in a blinded fashion by 3 independent pathologists. Serum TSP2 levels were measured by enzyme-linked immunosorbent assay.

RESULTS

Serum TSP2 levels were significantly elevated in MetS (1.58 [1.07-2.20] ng/mL) compared with non-MetS (1.28 [0.84-1.73] ng/mL; P = .006) in obese patients and positively correlated with increasing number of the MetS components, fasting glucose, glycated hemoglobin, fasting insulin, C-peptide, and homeostatic model assessment of insulin resistance after adjustment of conventional confounders. Serum TSP2 levels differentiated MASH (1.74 [1.32-3.09] ng/mL) from the other non-MASH less severe groups: normal liver (1.41 [1.04-1.63] ng/mL), simple steatosis (1.45 [0.89-1.92] ng/mL), and borderline MASH (1.30 [0.99-2.17] ng/mL) (P < .05). Elevated serum TSP2 was positively associated with the severity of hepatic steatosis, inflammation, fibrosis, and abnormal liver function independent of age, sex and adiposity. Furthermore, high serum TSP2 identified at-risk MASH with area under the operating curve of 0.84 (95% CI 0.70-0.98).

CONCLUSION

Serum TSP2 is closely associated with severity and progression of MetS and MAFLD, and is a promising noninvasive biomarker for differentiating MASH from benign steatosis and identifying at-risk MASH patients among individuals with obesity.

Reprogramming of Hepatic Metabolism and Microenvironment in Nonalcoholic Steatohepatitis

Nonalcoholic fatty liver disease (NAFLD), a spectrum of metabolic liver disease associated with obesity, ranges from relatively benign hepatic steatosis to nonalcoholic steatohepatitis (NASH). The latter is characterized by persistent liver injury, inflammation, and liver fibrosis, which collectively increase the risk for end-stage liver diseases such as cirrhosis and hepatocellular carcinoma. Recent work has shed new light on the pathophysiology of NAFLD/NASH, particularly the role of genetic, epigenetic, and dietary factors and metabolic dysfunctions in other tissues in driving excess hepatic fat accumulation and liver injury. In parallel, single-cell RNA sequencing studies have revealed unprecedented details of the molecular nature of liver cell heterogeneity, intrahepatic cross talk, and disease-associated reprogramming of the liver immune and stromal vascular microenvironment. This review covers the recent advances in these areas, the emerging concepts of NASH pathogenesis, and potential new therapeutic opportunities. Expected final online publication date for the Annual Review of Nutrition, Volume 42 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

In-Depth Serum Proteomics by DIA-MS with In Silico Spectral Libraries Reveals Dynamics during the Active Phase of Systemic Juvenile Idiopathic Arthritis

In serum proteomics using mass spectrometry, the number of detectable proteins is reduced due to high-abundance proteins, such as albumin. However, recently developed data-independent acquisition mass spectrometry (DIA-MS) proteomics technology has made it possible to remarkably improve the number of proteins in a serum analysis by removing high-abundance proteins. Using this technology, we analyzed sera from patients with systemic juvenile idiopathic arthritis (sJIA), a rare pediatric disease. As a result, we identified 2727 proteins with a wide dynamic range derived from various tissue leakages. We also selected 591 proteins that differed significantly in their active phases. These proteins were involved in many inflammatory processes, and we also identified immunoproteasomes, which were not previously found in serum, suggesting that they may be involved in the pathogenesis of sJIA. A detailed high-depth DIA-MS proteomic analysis of serum may be useful for understanding the pathogenesis of sJIA and may provide clues for the development of new biomarkers.

Diagnostic Modalities of Non-Alcoholic Fatty Liver Disease: From Biochemical Biomarkers to Multi-Omics Non-Invasive Approaches

Non-Alcoholic Fatty Liver Disease (NAFLD) is currently the most common cause of chronic liver disease worldwide, and its prevalence is increasing globally. NAFLD is a multifaceted disorder, and its spectrum includes steatosis to steatohepatitis, which may evolve to advanced fibrosis and cirrhosis. In addition, the presence of NAFLD is independently associated with a higher cardiometabolic risk and increased mortality rates. Considering that the vast majority of individuals with NAFLD are mainly asymptomatic, early diagnosis of non-alcoholic steatohepatitis (NASH) and accurate staging of fibrosis risk is crucial for better stratification, monitoring and targeted management of patients at risk. To date, liver biopsy remains the gold standard procedure for the diagnosis of NASH and staging of NAFLD. However, due to its invasive nature, research on non-invasive tests is rapidly increasing with significant advances having been achieved during the last decades in the diagnostic field. New promising non-invasive biomarkers and techniques have been developed, evaluated and assessed, including biochemical markers, imaging modalities and the most recent multi-omics approaches. Our article provides a comprehensive review of the currently available and emerging non-invasive diagnostic tools used in assessing NAFLD, also highlighting the importance of accurate and validated diagnostic tools.

Characterization and Pharmacological Validation of a Preclinical Model of NASH in Göttingen Minipigs

Background

Nonalcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease, which is associated with features of metabolic syndrome. NAFLD may progress in a subset of patients into nonalcoholic steatohepatitis (NASH) with liver injury resulting ultimately in cirrhosis and potentially hepatocellular carcinoma. Today, there is no approved treatment for NASH due to, at least in part, the lack of preclinical models recapitulating features of human disease. Here, we report the development of a dietary model of NASH in the Göttingen minipig.

Methods

First, we performed a longitudinal characterization of diet-induced NASH and fibrosis using biochemical, histological, and transcriptional analyses. We then evaluated the pharmacological response to Obeticholic acid (OCA) treatment for 8 weeks at 2.5mg/kg/d, a dose matching its active clinical exposure.

Results

Serial histological examinations revealed a rapid installation of NASH driven by massive steatosis and inflammation, including evidence of ballooning. Furthermore, we found the progressive development of both perisinusoidal and portal fibrosis reaching fibrotic septa after 6 months of diet. Histological changes were mechanistically supported by well-defined gene signatures identified by RNA Seq analysis. While treatment with OCA was well tolerated throughout the study, it did not improve liver dysfunction nor NASH progression. By contrast, OCA treatment resulted in a significant reduction in diet-induced fibrosis in this model.

Conclusions

These results, taken together, indicate that the diet-induced NASH in the Göttingen minipig recapitulates most of the features of human NASH and may be a model with improved translational value to prioritize drug candidates toward clinical development.

Defining NASH from a Multi-Omics Systems Biology Perspective

Non-alcoholic steatohepatitis (NASH) is a chronic liver disease affecting up to 6.5% of the general population. There is no simple definition of NASH, and the molecular mechanism underlying disease pathogenesis remains elusive. Studies applying single omics technologies have enabled a better understanding of the molecular profiles associated with steatosis and hepatic inflammation—the commonly accepted histologic features for diagnosing NASH, as well as the discovery of novel candidate biomarkers. Multi-omics analysis holds great potential to uncover new insights into disease mechanism through integrating multiple layers of molecular information. Despite the technical and computational challenges associated with such efforts, a few pioneering studies have successfully applied multi-omics technologies to investigate NASH. Here, we review the most recent technological developments in mass spectrometry (MS)-based proteomics, metabolomics, and lipidomics. We summarize multi-omics studies and emerging omics biomarkers in NASH and highlight the biological insights gained through these integrated analyses.

Circulating Thrombospondin-2 as a Novel Fibrosis Biomarker of Nonalcoholic Fatty Liver Disease in Type 2 Diabetes

OBJECTIVE

Preclinical studies have suggested that thrombospondin-2 (TSP2) is implicated in liver fibrosis. However, the clinical relevance of TSP2 in nonalcoholic fatty liver disease (NAFLD) remains undefined. Here, we investigated the cross-sectional and longitudinal associations of circulating TSP2 levels with advanced fibrosis (F3 or greater [≥FE] fibrosis) in NAFLD.

RESEARCH DESIGN AND METHODS

Serum TSP2 levels were measured in 820 patients with type 2 diabetes and NAFLD. All participants received vibration-controlled transient elastography (VCTE) at baseline to evaluate their hepatic steatosis and fibrosis using controlled attenuation parameter (CAP) and liver stiffness (LS) measurements, respectively. Among those without advanced fibrosis at baseline, reassessment VCTE was performed to determine whether ≥F3 fibrosis had developed over time. Multivariable logistic regression analysis was used to evaluate the cross-sectional and longitudinal associations of serum TSP2 level with ≥F3 fibrosis.

RESULTS

Baseline serum TSP2 level was independently associated with the presence of ≥F3 fibrosis (odds ratio [OR] 5.13, P < 0.001). The inclusion of serum TSP2 level significantly improved the identification of ≥F3 fibrosis by clinical risk factors. Over a median follow-up of 1.5 years, 8.8% developed ≥F3 fibrosis. Baseline serum TSP2 level was significantly associated with incident ≥F3 fibrosis (OR 2.82, P = 0.005), independent of other significant clinical risk factors of fibrosis progression, including BMI, platelet count, and CAP at baseline.

CONCLUSIONS

Circulating TSP2 level was associated with both the presence and the development of advanced fibrosis and might be a potentially useful prognostic biomarker for the development and progression of liver fibrosis in patients with type 2 diabetes and NAFLD.

Current Options and Future Directions for NAFLD and NASH Treatment

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with a broad spectrum ranging from simple steatosis to advanced stage of nonalcoholic steatohepatitis (NASH). Although there are many undergoing clinical trials for NAFLD treatment, there is no currently approved treatment. NAFLD accounts as a major causing factor for the development of hepatocellular carcinoma (HCC), and its incidence rises accompanying the prevalence of obesity and diabetes. Reprogramming of antidiabetic and anti-obesity medicine is a major treatment option for NAFLD and NASH. Liver inflammation and cellular death, with or without fibrosis account for the progression of NAFLD to NASH. Therefore, molecules and signaling pathways involved in hepatic inflammation, fibrosis, and cell death are critically important targets for the therapy of NAFLD and NASH. In addition, the avoidance of aberrant infiltration of inflammatory cytokines by treating with CCR antagonists also provides a therapeutic option. Currently, there is an increasing number of pre-clinical and clinical trials undergoing to evaluate the effects of antidiabetic and anti-obesity drugs, antibiotics, pan-caspase inhibitors, CCR2/5 antagonists, and others on NAFLD, NASH, and liver fibrosis. Non-invasive serum diagnostic markers are developed for fulfilling the need of diagnostic testing in a large amount of NAFLD cases. Overall, a better understanding of the underlying mechanism of the pathogenesis of NAFLD is helpful to choose an optimized treatment.