Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview

Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among non-diabetes

BACKGROUND

Insulin resistance, lipotoxicity, and mitochondrial dysfunction contribute to the pathogenesis of metabolic dysfunction-associated steatotic liver disease (MASLD). Mitochondrial dysfunction impairs oxidative phosphorylation and increases reactive oxygen species production, leading to steatohepatitis and hepatic fibrosis. Artificial intelligence (AI) is a potent tool for disease diagnosis and risk stratification.

AIM

To investigate mitochondrial DNA polymorphisms in susceptibility to MASLD and establish an AI model for MASLD screening.

METHODS

Multiplex polymerase chain reaction was performed to comprehensively genotype 82 mitochondrial DNA variants in the screening dataset (n = 264). The significant mitochondrial single nucleotide polymorphism was validated in an independent cohort (n = 1046) using the Taqman^® allelic discrimination assay. Random forest, eXtreme gradient boosting, Naive Bayes, and logistic regression algorithms were employed to construct an AI model for MASLD.

RESULTS

In the screening dataset, only mt12361A>G was significantly associated with MASLD. mt12361A>G showed borderline significance in MASLD patients with 2-3 cardiometabolic traits compared with controls in the validation dataset (P = 0.055). Multivariate regression analysis confirmed that mt12361A>G was an independent risk factor of MASLD [odds ratio (OR) = 2.54, 95% confidence interval (CI): 1.19-5.43, P = 0.016]. The genetic effect of mt12361A>G was significant in the non-diabetic group but not in the diabetic group. mt12361G carriers had a 2.8-fold higher risk than A carriers in the non-diabetic group (OR = 2.80, 95%CI: 1.22-6.41, P = 0.015). By integrating clinical features and mt12361A>G, random forest outperformed other algorithms in detecting MASLD [training area under the receiver operating characteristic curve (AUROC) = 1.000, validation AUROC = 0.876].

CONCLUSION

The mt12361A>G variant increased the severity of MASLD in non-diabetic patients. AI supports the screening and management of MASLD in primary care settings.

Advances in research on metabolic dysfunction-associated steatotic liver disease

The global increase in obesity-related metabolic disorders has led to metabolic dysfunction-associated steatotic liver disease (MASLD) emerging as one of the most prevalent chronic liver disease worldwide. Despite growing concerns, the exact pathogenesis of MASLD remains unclear and no definitive treatments have been made available. Consequently, the need for comprehensive research on MASLD is more critical than ever. Gaining insight into the mechanisms of the disease can lay the groundwork for identifying new therapeutic targets and can facilitate the development of diagnostic tools that enable the early detection and intervention of MASLD. Research has discovered a multifactorial etiology for MASLD, suggesting that potential therapeutic strategies should be considered from a variety of perspectives. This review delves into the pathogenesis of MASLD, current diagnostic approaches, potential therapeutic targets, the status of clinical trials for emerging drugs, and the most promising treatment methods available today. With a focus on therapeutic targets, the aim is to offer fresh insights and guide for future research in the treatment of MASLD.

Multi-omics profiling reveals altered mitochondrial metabolism in adipose tissue from patients with metabolic dysfunction-associated steatohepatitis

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) and its more severe form steatohepatitis (MASH) contribute to rising morbidity and mortality rates. The storage of fat in humans is closely associated with these diseases' progression. Thus, adipose tissue metabolic homeostasis could be key in both the onset and progression of MASH.

METHODS

We conducted a case-control observational research using a systems biology-based approach to analyse liver, abdominal subcutaneous adipose tissue (SAT), omental visceral adipose tissue (VAT), and blood of n = 100 patients undergoing bariatric surgery (NCT05554224). MASH was diagnosed through histologic assessment. Whole-slide image analysis, lipidomics, proteomics, and transcriptomics were performed on tissue samples. Lipidomics and proteomics profiles were determined on plasma samples.

FINDINGS

Liver transcriptomics, proteomics, and lipidomics revealed interconnected pathways associated with inflammation, mitochondrial dysfunction, and lipotoxicity in MASH. Paired adipose tissue biopsies had larger adipocyte areas in both fat depots in MASH. Enrichment analyses of proteomics and lipidomics data confirmed the association of liver lesions with mitochondrial dysfunction in VAT. Plasma lipidomics identified candidates with high diagnostic accuracy (AUC = 0.919, 95% CI 0.840-0.979) for screening MASH.

INTERPRETATION

Mitochondrial dysfunction is also present in VAT in patients with obesity-associated MASH. This may cause a disruption in the metabolic equilibrium of lipid processing and storage, which impacts the liver and accelerates detrimental adaptative responses.

FUNDING

The project leading to these results has received funding from 'la Caixa' Foundation (HR21-00430), and from the Instituto de Salud Carlos III (ISCIII) (PI21/00510) and co-funded by the European Union.

Metabolic signatures of metabolic dysfunction-associated steatotic liver disease in severely obese patients

BACKROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) can lead to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Still, most patients with MASLD die from cardiovascular diseases indicating metabolic alterations related to both liver and cardiovascular pathology.

AIMS AND METHODS

The aim of this study was to assess biologic pathways behind MASLD progression from steatosis to metabolic dysfunction-associated steatohepatitis (MASH) using non-targeted liquid chromatography-mass spectrometry analysis in 106 severely obese individuals (78 women, mean age 47.7 7 ± 9.2 years, body mass index 41.8 ± 4.3 kg/m²) undergoing laparoscopic Roux-en-Y gastric bypass.

RESULTS

We identified several metabolites that are associated with MASLD progression. Most importantly, we observed a decrease of lysophosphatidylcholines LPC(18:2), LPC(18:3), and LPC(20:3) and increase of xanthine when comparing those with steatosis to those with MASH. We found that indole propionic acid and threonine were negatively correlated to fibrosis, but not with the metabolic disturbances associated with cardiovascular risk. Xanthine, ketoleucine, and tryptophan were positively correlated to lobular inflammation and ballooning but also with insulin resistance, and dyslipidemia, respectively. The results did not change when taking into account the most important genetic risk factors of MASLD.

CONCLUSIONS

Our findings suggest that there are several separate biological pathways, some of them independent of insulin resistance and dyslipidemia, associating with MASLD.

The role of artificial intelligence in the management of liver diseases

Universal neonatal hepatitis B virus (HBV) vaccination and the advent of direct-acting antivirals (DAA) against hepatitis C virus (HCV) have reshaped the epidemiology of chronic liver diseases. However, some aspects of the management of chronic liver diseases remain unresolved. Nucleotide analogs can achieve sustained HBV DNA suppression but rarely lead to a functional cure. Despite the high efficacy of DAAs, successful antiviral therapy does not eliminate the risk of hepatocellular carcinoma (HCC), highlighted the need for cost-effective identification of high-risk populations for HCC surveillance and tailored HCC treatment strategies for these populations. The accessibility of high-throughput genomic data has accelerated the development of precision medicine, and the emergence of artificial intelligence (AI) has led to a new era of precision medicine. AI can learn from complex, non-linear data and identify hidden patterns within real-world datasets. The combination of AI and multi-omics approaches can facilitate disease diagnosis, biomarker discovery, and the prediction of treatment efficacy and prognosis. AI algorithms have been implemented in various aspects, including non-invasive tests, predictive models, image diagnosis, and the interpretation of histopathology findings. AI can support clinicians in decision-making, alleviate clinical burdens, and curtail healthcare expenses. In this review, we introduce the fundamental concepts of machine learning and review the role of AI in the management of chronic liver diseases.

Lipidomic Analysis Reveals Branched-Chain and Cyclic Fatty Acids from Angomonas deanei Grown under Different Nutritional and Physiological Conditions

Angomonas deanei belongs to Trypanosomatidae family, a family of parasites that only infect insects. It hosts a bacterial endosymbiont in a mutualistic relationship, constituting an excellent model for studying organelle origin and cellular evolution. A lipidomic approach, which allows for a comprehensive analysis of all lipids in a biological system (lipidome), is a useful tool for identifying and measuring different expression patterns of lipid classes. The present study applied GC-MS and NMR techniques, coupled with principal component analysis (PCA), in order to perform a comparative lipidomic study of wild and aposymbiotic A. deanei grown in the presence or absence of FBS. Unusual contents of branched-chain iso C17:0 and C19:0-cis-9,10 and-11,12 fatty acids were identified in A. deanei cultures, and it was interesting to note that their content slightly decreased at the log phase culture, indicating that in the latter growth stages the cell must promote the remodeling of lipid synthesis in order to maintain the fluidity of the membrane. The combination of analytical techniques used in this work allowed for the detection and characterization of lipids and relevant contributors in a variety of A. deanei growth conditions.

Effects of Combined Low-Dose Spironolactone Plus Vitamin E versus Vitamin E Monotherapy on Lipidomic Profile in Non-Alcoholic Fatty Liver Disease: A Post Hoc Analysis of a Randomized Controlled Trial

Background/Objectives: Lipid dysmetabolism seems to contribute to the development and progression of nonalcoholic fatty liver disease (NAFLD). Our aim was to compare serum lipidomic profile between patients with NAFLD having received monotherapy with vitamin E (400 IU/d) and those having received combination therapy with vitamin E (400 IU/d) and low-dose spironolactone (25 mg/d) for 52 weeks. Methods: This was a post hoc study of a randomized controlled trial (NCT01147523). Serum lipidomic analysis was performed in vitamin E monotherapy group (n = 15) and spironolactone plus vitamin E combination therapy group (n = 12). We employed an untargeted liquid chromatography-mass spectrometry lipid profiling approach in positive and negative ionization mode. Results: Univariate analysis revealed 36 lipid molecules statistically different between groups in positive mode and seven molecules in negative mode. Multivariate analysis in negative mode identified six lipid molecules that remained robustly different between groups. After adjustment for potential confounders, including gender, omega-3 supplementation, leptin concentration and homeostasis model assessment-insulin resistance (HOMA-IR), four lipid molecules remained significant between groups: FA 20:5, SM 34:2;O2, SM 42:3;O2 and CE 22:6, all being higher in the combination treatment group. Conclusions: The combination of spironolactone with vitamin E led to higher circulating levels of four lipid molecules than vitamin E monotherapy, after adjustment for potential confounders. Owing to very limited relevant data, we could not support that these changes in lipid molecules may be beneficial or not for the progression of NAFLD. Thus, mechanistic studies are warranted to clarify the potential clinical significance of these findings.

Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach

Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation is considered the hallmark of NAFLD and is associated with lipotoxicity and inflammation, as well as increased oxidative stress levels. In this study, a lipidomic approach was used to investigate the plasma lipidome of 12 NASH patients, 10 Nonalcoholic Fatty Liver (NAFL) patients, and 15 healthy controls, revealing significant alterations in lipid classes, such as glycerolipids and glycerophospholipids, as well as fatty acid compositions in the context of steatosis and steatohepatitis. A machine learning XGBoost algorithm identified a panel of 15 plasma biomarkers, including HOMA-IR, BMI, platelets count, LDL-c, ferritin, AST, FA 12:0, FA 18:3 ω3, FA 20:4 ω6/FA 20:5 ω3, CAR 4:0, LPC 20:4, LPC O-16:1, LPE 18:0, DG 18:1_18:2, and CE 20:4 for predicting steatohepatitis. This research offers insights into the connection between imbalanced lipid metabolism and the formation and progression of NAFL D, while also supporting previous research findings. Future studies on lipid metabolism could lead to new therapeutic approaches and enhanced risk assessment methods, as the shift from isolated steatosis to NASH is currently poorly understood.

Development of a novel non-invasive biomarker panel for hepatic fibrosis in MASLD

Accurate non-invasive biomarkers to diagnose metabolic dysfunction-associated steatotic liver disease (MASLD)-related fibrosis are urgently needed. This study applies a translational approach to develop a blood-based biomarker panel for fibrosis detection in MASLD. A molecular gene expression signature identified from a diet-induced MASLD mouse model (LDLr-/-.Leiden) is translated into human blood-based biomarkers based on liver biopsy transcriptomic profiles and protein levels in MASLD patient serum samples. The resulting biomarker panel consists of IGFBP7, SSc5D and Sema4D. LightGBM modeling using this panel demonstrates high accuracy in predicting MASLD fibrosis stage (F0/F1: AUC = 0.82; F2: AUC = 0.89; F3/F4: AUC = 0.87), which is replicated in an independent validation cohort. The overall accuracy of the model outperforms predictions by the existing markers Fib-4, APRI and FibroScan. In conclusion, here we show a disease mechanism-related blood-based biomarker panel with three biomarkers which is able to identify MASLD patients with mild or advanced hepatic fibrosis with high accuracy.

Identification of signature gene set as highly accurate determination of metabolic dysfunction-associated steatotic liver disease progression

BACKGROUND/AIMS

Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression.

METHODS

Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD.

RESULTS

After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort.

CONCLUSION

We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.

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.

Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review

This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.

Frizzled receptors and SFRP5 in lipid metabolism: Current findings and potential applications

Lipid metabolism plays a very important role as the central metabolic process of the body. Lipid metabolism interruptions may cause many chronic diseases, for example, non-alcoholic fatty liver disease (NAFLD), diabetes, and obesity. Secreted Frizzled Related Protein 5 (SFRP5) and Frizzled receptors (FZD) are two newly discovered adipokines that are involved in lipid metabolism as well as lipogenesis. Both of these adipokines affect lipid metabolism and adipogenesis through three WNT signaling pathways (WNTSP): WNT/β-catenin, WNT/Ca²⁺, and WNT/JNK. FZD consists of 10 species, which have a cysteine-rich domain (CRD) to bind to the WNT protein for signal transduction. Depending on the type of ligand or co-receptor, they can stimulate or inhibit adipogenesis. In lipid metabolism, they play a role in recognizing fatty acids. In obesity, gene expression of the WNT/FZD receptors is significantly increased. In contrast, SFPR5 serves as an antagonist that can compete with FZD for inhibition of WNTSP. It is believed to have anti-inflammatory potential in obesity and diseases related to abnormal lipid metabolism. In these cases, the expression of SFRP5 is found to be very low leading to the promoted production of proinflammatory cytokines (PICS). Some methods that include using recombinant SFRP5 to improve non-alcoholic steatohepatitis (NASH), using secreted Ly-6/uPAR-related protein 1 (Slurp1) to regulate fat accumulation in the liver through SFRP5, and dietary and lifestyle interventions to improve overweight/obesity have been studied. However, understandings of the molecular mechanisms of these two adipokines and their interactions are very limited. Therefore, more in-depth studies are needed in the future.

Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19

Viral infections cause metabolic dysregulation in the infected organism. The present study used metabolomics techniques and machine learning algorithms to retrospectively analyze the alterations of a broad panel of metabolites in the serum and urine of a cohort of 126 patients hospitalized with COVID-19. Results were compared with those of 50 healthy subjects and 45 COVID-19-negative patients but with bacterial infectious diseases. Metabolites were analyzed by gas chromatography coupled to quadrupole time-of-flight mass spectrometry. The main metabolites altered in the sera of COVID-19 patients were those of pentose glucuronate interconversion, ascorbate and fructose metabolism, nucleotide sugars, and nucleotide and amino acid metabolism. Alterations in serum maltose, mannonic acid, xylitol, or glyceric acid metabolites segregated positive patients from the control group with high diagnostic accuracy, while succinic acid segregated positive patients from those with other disparate infectious diseases. Increased lauric acid concentrations were associated with the severity of infection and death. Urine analyses could not discriminate between groups. Targeted metabolomics and machine learning algorithms facilitated the exploration of the metabolic alterations underlying COVID-19 infection, and to identify the potential biomarkers for the diagnosis and prognosis of the disease.

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.

Measurement of lipid flux to advance translational research: evolution of classic methods to the future of precision health

Over the past 70 years, the study of lipid metabolism has led to important discoveries in identifying the underlying mechanisms of chronic diseases. Advances in the use of stable isotopes and mass spectrometry in humans have expanded our knowledge of target molecules that contribute to pathologies and lipid metabolic pathways. These advances have been leveraged within two research paths, leading to the ability (1) to quantitate lipid flux to understand the fundamentals of human physiology and pathology and (2) to perform untargeted analyses of human blood and tissues derived from a single timepoint to identify lipidomic patterns that predict disease. This review describes the physiological and analytical parameters that influence these measurements and how these issues will propel the coming together of the two fields of metabolic tracing and lipidomics. The potential of data science to advance these fields is also discussed. Future developments are needed to increase the precision of lipid measurements in human samples, leading to discoveries in how individuals vary in their production, storage, and use of lipids. New techniques are critical to support clinical strategies to prevent disease and to identify mechanisms by which treatments confer health benefits with the overall goal of reducing the burden of human disease.

Personalized tracking of how lipid (fat) metabolism changes over time could lead to improvements in the diagnosis and treatment of several diseases. Elizabeth Parks and colleagues from the University of Missouri, Columbia, USA, discuss the ways in which researchers use stable isotope labeling to monitor the kinetics of fatty acids and other lipids in the body. Usually, lipid quantities are measured only at a single timepoint, however the tracking of lipid turnover over time provides further diagnostic information. Aided by new techniques such as high-throughput mass spectrometry and machine learning, researchers are now able to continuously map total lipid contents in individual patients. The transition of measurements of lipid flux from the research laboratory to the doctor’s office will likely play a role in a new era of precision medicine.

Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review

Non-alcoholic fatty liver disease (NAFLD) is one of the most important causes of chronic liver disease in the world, it has been found that cardiovascular and renal risks and diseases are also highly prevalent in adults with NAFLD. Diagnosis and treatment of NAFLD face many challenges, although the medical science has been very developed. Efficiency, accuracy and individualization are the main goals to be solved. Evaluation of the severity of NAFLD involves a variety of clinical parameters, how to optimize non-invasive evaluation methods is a necessary issue that needs to be discussed in this field. Artificial intelligence (AI) has become increasingly widespread in healthcare applications, and it has been also brought many new insights into better analyzing chronic liver disease, including NAFLD. This paper reviewed AI related researches in NAFLD field published recently, summarized diagnostic models based on electronic health record and lab test, ultrasound and radio imaging, and liver histopathological data, described the application of therapeutic models in personalized lifestyle guidance and the development of drugs for NAFLD. In addition, we also analyzed present AI models in distinguishing healthy VS NAFLD/NASH, and fibrosis VS non-fibrosis in the evaluation of NAFLD progression. We hope to provide alternative directions for the future research.

Metabolomics in Bariatric and Metabolic Surgery Research and the Potential of Deep Learning in Bridging the Gap

During the past several years, there has been a shift in terminology from bariatric surgery alone to bariatric and metabolic surgery (BMS). More than a change in name, this signifies a paradigm shift that incorporates the metabolic effects of operations performed for weight loss and the amelioration of related medical problems. Metabolomics is a relatively novel concept in the field of bariatrics, with some consistent changes in metabolite concentrations before and after weight loss. However, the abundance of metabolites is not easy to handle. This is where artificial intelligence, and more specifically deep learning, would aid in revealing hidden relationships and would help the clinician in the decision-making process of patient selection in an individualized way.

Afamin Levels and Their Correlation with Oxidative and Lipid Parameters in Non-diabetic, Obese Patients

Background: Afamin is a liver-produced bioactive protein and features α- and γ-tocopherol binding sites. Afamin levels are elevated in metabolic syndrome and obesity and correlate well with components of metabolic syndrome. Afamin concentrations, correlations between afamin and vitamin E, afamin and lipoprotein subfractions in non-diabetic, obese patients have not been fully examined. Methods: Fifty non-diabetic, morbidly obese patients and thirty-two healthy, normal-weight individuals were involved in our study. The afamin concentrations were measured by ELISA. Lipoprotein subfractions were determined with gel electrophoresis. Gas chromatography–mass spectrometry was used to measure α- and γ tocopherol levels. Results: Afamin concentrations were significantly higher in the obese patients compared to the healthy control (70.4 ± 12.8 vs. 47.6 ± 8.5 μg/mL, p < 0.001). Positive correlations were found between afamin and fasting glucose, HbA1c, hsCRP, triglyceride, and oxidized LDL level, as well as the amount and ratio of small HDL subfractions. Negative correlations were observed between afamin and mean LDL size, as well as the amount and ratio of large HDL subfractions. After multiple regression analysis, HbA1c levels and small HDL turned out to be independent predictors of afamin. Conclusions: Afamin may be involved in the development of obesity-related oxidative stress via the development of insulin resistance and not by affecting α- and γ-tocopherol levels.