Abstract

INTRODUCTION

The liver is a key organ involved in relevant homeostatic metabolic and detoxifying human functions[1]. Thus, the liver is the epicenter of an organ-organ network weaving a series of complex interactions in the organism, which makes liver damage an underlying adverse condition in a whole set of diseases. Chronic liver disease (CLD) can be caused mainly by alcoholic liver-related dysfunctions, hepatitis B virus (HBV), hepatitis C virus (HCV), drug treatments, or non-alcoholic fatty liver disease (NAFLD), as recently updated to the term metabolic-associated FLD (Figure ​(Figure11)[2,3]. Patients with liver-related diseases need frequent follow-ups and careful monitoring since CLD can eventually lead to cirrhosis or hepatocellular carcinoma (HCC) if not diagnosed on time for treatment or surgery. These CLD-related conditions have become a global burden, whose mortality associated rates have increased over the years reaching more than 2 million deaths worldwide[4].

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Figure 1

Factors involved in the development of chronic liver disease triggering associated processes that lead to increased fibrosis stage.

CLD is usually accompanied by an unhealthy inflammatory environment[5]. The immune response is a fundamental process to maintain homeostasis within the organism defense machinery and is characterized by the secretion of proinflammatory cytokines, like interleukin (IL)-1, tumor necrosis factor-α (TNF-α), and prostaglandin E2, in an acute manner in order to resolve sudden damage[5]. However, if sustained over time, these abnormal levels of inflammatory cytokines cause low-grade inflammation (LGI). LGI is a silent condition that predisposes to the development of metabolic and infectious diseases that has become a worldwide health issue[6]. Patients with CLD, such as non-alcoholic steatohepatitis (NASH), present impaired immune function, dysbiosis, insulin resistance (IR) and LGI, all of which can aggravate infectious disease progression and perpetuate excess of adipose tissue, are characterized by overstimulation of the production of adipose-derived inflammatory molecules[5,7-9].

The liver also secretes important hepatokines that act as signaling proteins modulating functions in other organs and are involved in a wide range of conditions, such as IR and adipogenesis[1]. For instance, fibroblast growth factor-21 (FGF-21) is a mediator participating in glucose metabolism mainly secreted by the liver that modulates adipogenesis, while fetuins, liver-derived plasma proteins, are participating in metabolic impairment and inflammation[1]. A dysregulation in systemic cytokines prompts fat accumulation in hepatocytes, which in turn promotes local secretion of proinflammatory hepatokines, leading to liver steatosis and IR. In addition, immune cells also find difficulty in this inflammatory environment to exert their role appropriately. Persistent inflammatory signals over time also abnormally activate immune cells, impairing the body’s ability to fight infection, repair tissue damage, or recover from possible poisoning. Inflammation comes hand in hand with an increase in oxidative stress, a state characterized by an imbalance in favoring the accumulation of higher reactive oxygen (ROS) and nitrogen species. These molecules in unusual concentrations damage the cell and environmental milieu by promoting the expression of proinflammatory genes, resulting in a vicious cycle. Thus, CLD presents an oxidative atmosphere, probably linked to the proinflammatory state[10,11]. This environment is the perfect setting for the fibrogenic process to unfold, an underlying condition of CLD that is characterized by progressive accumulation of fibrillar extracellular matrix in the liver[12]. The stage of hepatic fibrosis has been associated with the risk of mortality and liver-related morbidity in patients with NAFLD[13], virus-induced hepatitis[14,15], and alcoholic-derived liver disease[16], eventually leading to HCC.

In this context, infection by human hepatitis viruses (HHVs) is the most common cause of hepatitis, leading to the activation of the immune system, and the subsequent inflammatory response[17]. HBV and HCV acute infections can be resolved with antiviral and immune therapy. However, in a significant percentage they can progress to chronic hepatitis. This persistent infection can lead to comorbidities outside the liver, like arthritis, vasculitis, myalgia, and peripheral neuropathies[18]. Moreover, another new infectious disease appeared in late 2019 that can cause liver damage: Coronavirus disease 2019 (COVID-19). COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and it has become a global health issue since its outbreak in 2020 was declared a pandemic. Beyond lung function, COVID-19 can affect a wide variety of tissues, like the gastrointestinal tract, kidneys, and liver, with an underlying adverse inflammatory environment[19]. This inflammatory-related condition has been strongly associated to metabolic status and worsening diseases like obesity, diabetes, and hypertension[7,20-22]. For instance, COVID-19 can increase hepatic lipid accumulation by mitochondrial and endoplasmic reticulum (ER) dysfunction or worsen NAFLD if it was already present. A recent systematic review depicted that the parameters normally used for liver impairment screening were significantly increased in COVID-19 patients[23], placing CLD as a risk factor for progressive and severe COVID-19[24,25].

CLD is a global health problem, and new methods are needed to tackle this life-threatening condition. In this line, this review aims to explore machine learning (ML)-based approaches to manage CLD and develop biomarkers for diagnosis and prognosis. Its goal is to shed light on the factors involved in CLD to help health professionals in clinical management with the support of ML and identify new targets that can define therapeutic care lines in viral infections and non-communicable diseases (NCD), with an impact on liver functions with an inflammatory component. This includes the new disease, COVID-19.

MECHANISMS BY WHICH NCD AND INFLAMMATORY/IR PHENOMENA CAN AFFECT LIVER FUNCTION

The incidence of NCD, such as cardiovascular diseases and diabetes, has skyrocketed in the last decades, pressing authorities to establish developmental goals to achieve in the near future in terms of decreasing NCD-caused mortality[26]. Some of the risk factors that contribute to the development of NCD are excess of adipose tissue and high levels of glycemia. In this context, adipose tissue plays a key role in the development of FLD by secreting adipokines and other molecules, like free fatty acids (FFA)[8].

An energy excess prompts fat accumulation in the organism and the subsequent dysregulation of this tissue. This is of relevance since an inflamed adipose tissue results in increased levels of FFA and pro-inflammatory cytokines, IR, and infiltration of macrophages in the liver by the activation of Th1 and Th17 cells[8]. FFA enter the liver through the portal vein and trigger a series of reactions. For instance, they serve as ligands to toll-like receptor-4 complex, stimulating the production of TNF-α through the activation of nuclear factor-kappa B, favoring an inflammatory environment. Moreover, the excess of fat drives the polarization state of this increased number of macrophages from anti-inflammatory M2 to proinflammatory M1 macrophages and prompts fat accumulation in the liver and IR[8]. Adipose-derived macrophages also secrete inflammatory molecules, like TNF-α and IL-6, and adipokines, such as visfatin [also named nicotinamide phosphoribosyl transferase (NAMPT)]. NAMPT has gained relevance as a pivotal molecule linking adipose tissue and FLD. NAMPT is a pleiotropic molecule that can be found in an extracellular (eNAMPT) or an intracellular (iNAMP) form. Studies indicate that eNAMPT has enzyme and cytokine-like activity, stimulating the release of proinflammatory cytokines. Meanwhile, iNAMPT catalyzes the rate-limiting step in nicotinamide adenine dinucleotide (NAD+) formation. Because of this NAD+ boosting property, levels of iNAMPT have been proposed as beneficial for the homeostasis of the cell due to influencing the activity of NAD-dependent enzymes, such as sirtuins (SIRT). Remarkably, SIRT1 plays a key role in the liver by modulating the acetylation status of target molecules in lipid metabolism[27].

Furthermore, IR is characterized by hyperglycemia and the subsequent hyperinsulinemia to counteract high glucose levels, being a risk factor for NCDs, particularly type 2 diabetes, where it has been closely linked to oxidative stress[28]. A normal insulin signaling pathway starts with the activation of the insulin receptor so that it can bind to phosphoinositide 3-kinase to ultimately activate protein kinase B (Akt). Activated Akt drives glucose entry into the cell by promoting GLUT4 expression and glycogen synthesis[29]. Oxidative stress impairs this signal transduction through many different mechanisms, like inhibiting the transcription factors insulin promoter factor 1 and peroxisome proliferator-activated receptor gamma, which mediate insulin and GLUT-4 expression, respectively. Moreover, under hyperglycemic conditions, fetuin A hepatokine inhibits the insulin receptor and promotes inflammation, while FGF-21 inhibits lipid accumulation and increases insulin sensitivity. Dysregulation of this hormones, together with oxidative stress imbalance, lead to impaired insulin signaling[30].

The metabolic conditions underlying the development of NCD are complex, and they often reinforce each other, perpetuating an inflammatory environment and oxidative stress imbalance. As the orchestrating organ, these processes converge in the liver, affecting metabolic functions and setting the basis for the onset of the fibrogenic process characteristic of CLD.

MECHANISMS BY WHICH VIRAL INFECTIONS AND INFLAMMATORY/IR PHENOMENA CAN AFFECT LIVER FUNCTION

Persistent virus-associated liver damage can progress to CLD, which pressures health systems with a big social and economic burden. Although lots of resources have been invested to study the molecular mechanisms that mediate this process, results are diverse and still under investigation by the scientific community. HHVs directly infect hepatocytes, and the internalization into the cell is believed to happen by endocytosis, requiring the interaction with several host cell factors[17]. However, viral entry of HBV and HCV within hepatocytes is unclear, and further research is needed to elucidate this question. Sodium taurocholate co-transporting polypeptide was recently identified as an HBV receptor that would mediate HBV cell entry[31]. In the case of HCV, specific intercellular adhesion molecules appear key to cell adhesion and subsequent internalization[32].

Regarding HBV and HCV replication, it has been found that liver X receptor-α (LXR-α) plays a key role. LXR-α is a transcription factor whose activation triggers the expression of different genes that directly or indirectly modulate these viruses’ replication as well as the lipid and inflammatory alterations associated to CLD[33]. This inflammation is also mediated by the nucleotide-binding oligomerization domain-like receptor protein 3, which is activated by the abnormal production of ROS after a viral infection occurs in the liver. This ROS increase is associated with a decreased expression of nuclear factor-e2-related factor-2, a transcription factor that regulates ROS/recepteur d’origine nantais balance by maintaining redox homeostasis. These alterations compromise the normal state of the cell, laying the foundations on which the fibrotic process of CLD begins[11].

In the case of COVID-19, the mechanisms by which liver damage can occur are more unclear, but it is widely accepted that inflammation plays a huge role. This infection can trigger an exaggerated immune response leading to an uncontrolled cytokine release, also known as a “cytokine storm”. It is characterized by abnormal levels of IL-6, IL-1, C-C motif chemokine ligand (CCL)-5, chemokine (C-X-C motif) ligand (CXCL)-8, CXCL-1, and TNF-α among others[19]. This inflammatory cascade affects bile duct function since cytokines like TNF-α, IL-1, and IL-6, can induce hepatocellular cholestasis by downregulating hepatobiliary uptake and excretory systems[34].

Furthermore, the presence of this inflammatory environment can upregulate the expression of angiotensin converting enzyme 2 (ACE2) receptor in different tissues, like the adipose tissue and the liver[35-39]. This is of relevance since ACE2 receptors are the main cell entrance of the SARS-CoV-2 virus, and they are present in different tissues. Particularly in the liver, the cholangiocytes (characteristic cells of the bile duct)[40], as well as liver vascular endothelial cells[41], express ACE2 receptors. Hepatocytes and cholangiocytes are permissive to the SARS-CoV-2 virus, mediating subsequent entrance into the liver[42]. Several studies have found that ACE2 expression in hepatocytes is increased under hypoxia[43], a frequent condition in COVID-19 patients, and fibrotic conditions[44]. Besides ACE2 receptors, transmembrane serine protease 2 (TMPRSS2) and paired basic amino acid cleaving enzyme (FURIN) have been noted as significant for infection in the liver[45,46]. In this context, ACE2 expression is increased in patients in HCV-related cirrhosis[44], whereas TMPRSS2 and FURIN expression are upregulated in patients with obesity and NAFLD[47]. Moreover, infection by SARS-CoV-2 increases glucose-regulated protein 78 and 94, two biomarkers of ER stress[48,49], and impairs mitochondrial function[50]. This process is of interest since this state has been associated with de novo lipogenesis in hepatocytes[51], which could eventually lead to steatosis in these patients.

The use of therapeutic drugs can be another underlying cause of liver damage[3]. Because of detoxifying functions, the liver is subject to drug-induced damage coming from a wide range of approved drugs. Oncology drugs account for most hepatotoxicity cases, followed by those used for infectious diseases[3]. Since the beginning of the COVID-19 pandemic, a wide range of different treatments (antivirals, antibiotics, antimalaria, or corticosteroids) have been used in the absence of an efficient drug to treat severe infections. This pharmacological administration could explain that drug-induced liver injury appears in nearly 25% of COVID-19 patients[23], a consequence to consider when addressing liver damage in this disease.

ML APPROACHES IN INFLAMMATORY AND LIVER-RELATED COMORBIDITIES IN NON-COMMUNICABLE AND VIRAL DISEASES

Despite all the advances in the mechanisms driving the onset of these diseases, new techniques to detect innovative biomarkers for diagnosis and prognosis as well as to discover novel drugs are needed, for example artificial intelligence (AI). AI seeks to mimic human behavior, and within this science, ML is the most common approach[52]. The advances in computational science in the last decades have permitted the development of powerful algorithms based on this science. ML algorithms are particularly relevant for biological research because they allow the processing and integration of the huge amount of data that the latest advances in this field have brought by applying statistical methods to enable machines to improve experiences. This methodological approach can be categorized into two big groups: Supervised and unsupervised learning. In supervised algorithms, data is tagged in order to train the algorithm and fit it appropriately, whereas if it is unsupervised, the algorithm learns patterns from unlabeled data[53]. ML algorithms are generally assessed by simple methodologies like sensitivity, specificity, and accuracy. While sensitivity evaluates the proportion of true positives correctly identified, specificity evaluates the proportion of true negatives. Meanwhile, the accuracy value indicates the number of times the model is correct[54].

Supervised algorithms can be divided into two categories depending on the purpose: Prediction, in which the algorithm is fed and trained predictive models to data; or classification, which consists in clustering data within explanatory groups[55,56]. Predictive algorithms are based on regression models, and the most used are linear and logistic regression (LR), support vector machine (SVM), support vector regression (SVR), extra tree regression (ETR), artificial neural networks (ANN), and decision trees (DT). Regression models analyze the influence of one or multiple variables on a nominal or ordinal categorical outcome. ANN are more complex mathematical models (deep learning algorithms) that mimic the brain neural network, like the convolutional neural network (CNN), in which an input is fed through a hidden layer of many different well connected and structured nodes to produce a final output. In deep neuronal network (DNN) models, a great number of hidden successive layers use the output from the previous layer as input in a more complex algorithm. DT can also classify data, like random forest (RF) or gradient boosting (GB) models. Instead of minimizing error, these models determine thresholds derived from input data, assigning weight values to variables. Other models of classification are the Ada-Boost, Bayesian network (BN), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and linear discriminant analysis (LDA) that group data into clusters[55,56]. All these models can shed light into biological questions and are normally used indistinctively to obtain the best performance with the same dataset. For instance, Mijwil and Aggarwal[57] analyzed and compared 7 ML algorithms to predict appendix illness in the same dataset, revealing that certain models performed better than others, allowing for higher accuracy and results.

In FLD, the common techniques used in diagnostics are based on techniques like ultrasonography and magnetic resonance imagining (MRI). These methods are subjective, and the informed outcome mainly relies on the interpretation of the professional carrying out the procedure. Several investigations have studied the implementation of ML in order to classify FLD and other liver diseases by using images from ultrasounds, computed tomography (CT), and MRI[58,59]. However, the downside of this approach is that the quality of the images differs from one another because of several factors, such as equipment precision and interpersonal differences, for instance. Therefore, there is a need for ML approaches to help in image segmentation, and some authors have already implemented this technique to improve clinical practice[60,61].

Moreover, ML can help with the integration of more complex information beyond imaging to study and diagnose liver diseases since patients with CLD in the developmental phase require frequent follow-ups to check the progress of the disease and early detection changes in the diagnosis[58]. For example, patients with HHV-induced CLD are normally on antivirals. However, there is no consensus or guidelines about when to stop antiviral therapy or even if quitting these drugs will increase HCC risk. Therefore, new approaches need to be established to classify and prevent the development of more severe illnesses, like cirrhosis or cancer. In this line, ML approaches can be used to measure liver fibrosis, optimize diagnosis, and predict disease progression of CLD[62]. Table ​Table11 summarizes selected studies that have used ML for these purposes, which have been collected for this review, and Table ​Table22 summarizes the most repeated inputs from all compiled ML models along with the most repeated predictive results for the main four inflammation-related liver conditions.

PERSONALIZED MEDICINE IN LIVER-RELATED DISEASES SUPPORTED BY ML

In the early 21^st century, the Human Genome Project started the genomic era in which new disciplines like precision medicine appeared. Precision medicine aims to deliver targeted treatments based on a group of individual factors that greatly influence the onset and progression of a disease, like omics sciences. This approach covers a great number of patients, overcoming potential adverse effects and ensuring effectiveness of the treatment. In this context, computational advances have greatly contributed to the escalation of this science by lowering the costs of omics analysis and allowing the processing and integration of an enormous amount of data based on ML algorithms (Figure ​(Figure22).

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Figure 2

Data implicated in the onset of inflammatory-related liver diseases can be used to train machine learning algorithms for prediction, diagnosis, treatment, and prognosis of chronic liver disease, leading the way to personalized medicine.

ML has permitted the development of diagnostics and therapeutics based on the integration of omics data (genomics, epigenomic, transcriptomics, proteomics, metabolomics, and metagenomics) with clinical data. The ultimate goal is to bridge these omics data with the phenotype to bring molecular accuracy to the diagnosis, treatment, prognosis, and recurrence process of a pathological condition. This methodology has been used in a wide range of diseases in the search for more efficient and effective approaches, like heart and liver diseases[109,110]. For example, ML algorithms fed with omics data have been able to predict mortality in patients with alcoholic hepatitis. In this study, routine clinical variables of 210 patients with this disease were used to build six different datasets to assess mortality at 30 d and 90 d. Five different ML models were tested, obtaining the best performance in predicting 30-d mortality with a GB model using bacteria, MetaCyc pathways, and clinical data, as well as LR using viral and clinical data[111].

In hepatitis B, it has been found that ML algorithms can be very useful in assessing HBV-associated HCC progression. Ye et al[112] analyzed 67 HBV-positive HCC samples with or without intrahepatic metastases and discovered key genes for metastatic progression and survival training ML models. The majority of them were inflammatory or related to the inflammation process, like IL-2 receptor and osteopontin, which encodes an extracellular cytokine ligand whose overexpression favors metastasis. These authors were able for the first time to draw a molecular signature useful to classify metastatic HBV-HCC patients, opening the way for early detection and new treatments to increase patient survival. In hepatitis C, the CC and CT genotypes of the rs12979860 polymorphism in the IL28B gene have been associated with liver fibrosis progression, being able to predict antiviral treatment effectiveness[113].

Moreover, ML algorithms have allowed the diagnosis of advanced liver fibrosis according to the rs12979860 genotype with higher performance compared to APRI and FIB-4 scores[114]. In this study, patients were divided into two groups according to HCV-related liver fibrosis stage: None to moderate fibrosis (n = 204); or with advanced fibrosis (n = 223). ML algorithms revealed the IL28B genotype as the first predictor, while the second predictor depended on the mentioned genotype. For instance, in CT patients, PLT, albumin, and age were the determining variables, while for patients with the TT genotype, white blood cell count was the decisive feature to assess advanced fibrosis probability.

ML approaches have also helped to categorize obesity in different subtypes based on metabolic status[115-117]. For example, Masi et al[115] studied a cohort of 2567 subjects suffering from obesity and made clusters of metabolically healthy or metabolically unhealthy patients based on clinical and biochemical variables using two ML models. The first model showed that IR, body fat, HbA1c, red blood cells, age, ALT, uric acid, white blood cells, insulin growth factor-1, and GGT were the top predictors of a metabolically healthy obesity, revealing the importance of liver function.

Other authors have also used ML models to classify 882 obese patients in subtypes of obesity according to glucose, insulin, and uric acid levels[116]. Results showed four stable metabolic clusters in this cohort, which were characterized by a healthy metabolic status, or by hyperuricemia, hyperinsulinemia, and hyperglycemia, respectively. Furthermore, Lee et al[117] explored three-way interactions between genome, epigenome, and dietary/lifestyle factors using GB and RF models in a subset (n = 394) of the exam 8 of the Framingham Offspring Study cohort. Interestingly, GB obtained the best performance, revealing 21 single nucleotide polymorphisms, 230 methylation sites in relevant genes (like CPT1A, ABCG1, and SREBF1), and 26 dietary factors as top predictors for obesity. Intake of processed meat, artificially sweetened beverages, French fries, and alcohol intake, among other dietary factors, were highly associated with overweight/obesity.

Personalized and precision medicine aims to harmonize the greatest number of factors so that diagnosis, prognosis, and treatment are based on the greatest number of decision elements. Much remains to be investigated to establish guidelines in the context of personalized medicine. However, it is safe to say that precision medicine will drive modern medicine, combining the most classic variables with the newest digital variables. Health professionals must be prepared to understand and implement these new technologies in the near future.

CONCLUSION

In summary, ML science can process and integrate a vast amount of different data with unprecedented outstanding performance. The objective of this article was to collect the information derived from ML techniques in liver damage induced by inflammatory conditions, including the new disease COVID-19. The main role of ML in liver pathologies is to help identify high risk patients for referral to specialized centers. Results show that the use of ML models have brought new insights into biology and medicine questions that can be very useful in determining the next directions for research in diagnosis, prognosis, and treatment of inflammatory and virus-related liver diseases, leading the way to personalized medicine. Also inflammation/IR biomarkers related to liver disease can be boosted by ML strategies. This review clarified and compiled the importance of the different factors involved in CLD and analyzed by ML algorithms, which can be useful information for clinicians, like endocrinologists and gastroenterologists, and other healthcare professionals with a focus on hepatology and bioinformatics.

Footnotes

Contributor Information

J Alfredo Martínez, Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain.

Marta Alonso-Bernáldez, Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain.

Diego Martínez-Urbistondo, Department of Internal Medicine, Hospital Universitario HM Sanchinarro, Madrid 28050, Spain.

Juan A Vargas-Nuñez, Servicio de Medicina Interna, Hospital Universitario Puerta de Hierro Majadahonda, Madrid 28222, Majadahonda, Spain.

Ana Ramírez de Molina, Molecular Oncology and Nutritional Genomics of Cancer, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain.

Alberto Dávalos, Laboratory of Epigenetics of Lipid Metabolism, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain.

Omar Ramos-Lopez, Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, Mexico. [email protected].

References