Keywords big data - liver disease - cohorts - omics - artificial intelligence
New technologies like big data, artificial intelligence, and precision medicine are
changing how we understand and treat liver diseases. This review looks at how large
research studies and genetic data help scientists learn more about conditions like
steatotic liver disease. Artificial intelligence can sort patients into groups, suggest
better treatments, and even help find new drugs. Collaboration between researchers
and open data sharing are important to bring these discoveries into real-world healthcare.
Although these advances have great potential, they need careful planning to make sure
they truly help patients.
Introduction
In our current era of digital medicine, public databases are an indispensable resource
for understanding liver disease. Population-based databases are digital collections
of data that are available to researchers from all over the world,[1 ] often after submitting an application and usually for a fee. They are a crucial
tool in modern medical research, providing unparalleled access to information that
is essential for hypothesis generation, testing, or validation.[2 ] These new sources that often also include increasingly multi-omic data are of interest
in hepatology, as it has increased dimensionality and accessibility for researchers.
The role that large databases can play in research is diverse, allowing researchers
to perform various types of studies, including population-level comparative studies,
disease monitoring and phenotyping, predictive modeling to improve prognosis or risk-stratification,
or assessing unmet public health needs.[3 ] The integration of large-scale cohorts with increasingly sophisticated computational
methods has transformed the landscape of liver disease research. As these technologies
continue to evolve, their impact will only grow in the coming years.[4 ] By exploring the role of big data analytics in hepatology, we aim to highlight the
opportunities it presents for both clinicians and researchers while addressing the
challenges that must be overcome to fully unlock its potential.
Ongoing global scientific efforts aim to refine the classification of liver diseases
and establish well-characterized phenotypic subgroups based on shared traits or treatment
responses. Large-scale databases play a crucial role in this process, as they integrate
diverse datasets: this includes patient demographics, lifestyle information including
social history (i.e., alcohol intake, nutrition, pack years), genotypic information,
detailed clinical data from collected specimens, results of imaging procedures (ultrasound,
MRI, CT, or even FibroScan®), liver biopsy reports, microbiome data, and increasingly
often omics data (lipidomics, metabolomics, proteomics, transcriptomics, etc.). A
summary of these applications is depicted in [Fig. 1 ].
Fig. 1 How big data can change the workflow of hepatology research.
In population-based datasets, there are two different types of data: Unstructured
and structured.[5 ] Structured data consists of quantitative metrics organized within predefined formats,
such as electronic health records (EHRs), laboratory test results, and standardized
clinical measurements. These datasets can be readily accessible through databases
and are easier to analyze using conventional statistical and machine learning approaches.
However, unstructured data accounts for nearly 80% of all data and includes a wide
range of complex, non-tabular information, such as physician notes, imaging reports,
pathology slides, and genomic sequences. Unlike structured data, unstructured data
are not directly searchable or analyzable within traditional data management systems.
Extracting meaningful insights from these datasets requires advanced computational
techniques, including natural language processing (NLP) for text mining[6 ] or deep learning for medical imaging to extract radiomic data.[7 ] By utilizing newer technologies with language models,[8 ] it is expected that unstructured data can become easily structured by creating more
easily accessible summaries of medical information. A recent study demonstrated that
large language models (like GPT4) are highly accurate for identifying cirrhosis and
its complications from discharge summaries, outperforming traditional code-based classification,
suggesting they could potentially augment or replace labor-intensive chart reviews
in cirrhosis research cohort identification.[8 ] In hepatology, there is significant clinical utility in leveraging both structured
and unstructured data due to the complexity of liver diseases and the need for comprehensive
patient assessment.[9 ] Structured data, such as laboratory values (e.g., ALT, AST, bilirubin), imaging-derived
biomarkers (e.g., liver stiffness from elastography), and standardized clinical scores
(e.g., MELD, FIB-4), provide quantitative measures that facilitate disease classification,
prognosis, and treatment decision-making.[10 ] Conversely, unstructured data, including physician notes, pathology reports, imaging
scans, and endoscopic findings, contain valuable contextual information that structured
data alone cannot capture.[11 ] Despite its clinical significance, unstructured data are often not systematically
included in population-based datasets, limiting its potential for large-scale studies.
There are various types of population-based databases relevant to liver research,
as summarized in [Table 1 ]. Many of these databases have been collecting data over years or even decades, enabling
longitudinal studies that provide valuable insights into disease progression and long-term
outcomes.
Table 1
Some databases that may be relevant to liver research, with details of their respective
data types
Region
Biobank
Number of participants
Type of data
Special features
Relevance for hepatology
Europe
KORA[73 ] (Cooperative Health Research in the Augsburg Region)
Over 18,000
Clinical, genetic, and environmental data
Focus on cardiovascular and environmental research
Data on liver disease and metabolic syndrome
LURIC[74 ] (Ludwigshafen Risk and Cardiovascular Health Study)
Over 3,300
Genetic, metabolic, and inflammatory data
Focus on cardiovascular diseases
Information on interactions between cardiovascular and liver diseases
NAKO[75 ] (German National Cohort)
Approximately 200,000
Clinical, genetic, and lifestyle data
One of the largest population-based studies in Germany
Comprehensive data on liver disease
UK Biobank[76 ]
Over 500,000
Genetic, clinical, and lifestyle data
Extensive genetic and medical data
Genetics, liver MRI, “omics”
FinnGen[77 ]
Over 500,000
Genetic and disease registry data
Combination of genetic and registry data
Genetics, “omics”
Lifelines[78 ]
Over 167,000
Clinical, questionnaire, biological data Microbiome
Longitudinal study on health and disease
Longitudinal data on the development of liver disease and microbiota
Biobank Graz[79 ]
Over 7.5 million samples
Biological samples, clinical data
One of the largest biobanks in Europe
Liver samples
DeCODE Genetics[80 ]
Over 160,000
Genetic and clinical data
Focus on the population of Iceland with extensive genealogical information
Genetic predispositions to liver diseases
Our Future Health[81 ]
Target of 5 million
Genetic, clinical, and lifestyle data
Aims to be the UK's largest health research program
Potential for large-scale studies on liver disease prevention and treatment
United States
Penn Medicine Biobank[82 ]
60,000
Genetic and clinical data
Focus on personalized medicine and research
Data on genetic markers and personalized medicine in liver disease
Mass General Brigham Biobank[83 ]
Over 145,000
Genetic, clinical, and lifestyle data
Large-scale repository
Provides data for studies on liver disease risk factors and genetics
Mayo Clinic Biobank[84 ]
Over 21,000
Genetic, clinical, and lifestyle data
Comprehensive health data with a focus on individualized medicine
Resource for studies on genetic predispositions to liver diseases
Kaiser Permanente Research Bank[85 ]
Over 300,000
Genetic, clinical, and environmental data
Emphasis on health research across diverse populations
Enables studies on environmental and genetic factors in cancer
All of Us[86 ]
Target of 1 million
Genetic, environmental, and lifestyle data
Diversity and inclusion of a broad population
Diverse data on environmental and lifestyle factors that influence liver disease
Million Veteran Program[87 ]
Over 825,000
Genetic and military exposure data
Study of genetic and environmental factors in veterans
Effects of environmental and lifestyle factors on liver disease
NHANES[88 ]
Annually approximately 5,000
Clinical, nutritional, and health data
Longitudinal study on health and nutrition in the USA
Diet and lifestyle data, FibroScan
MyCode Geisinger Health System[89 ]
Over 200,000
Genetic and clinical data
System-wide biobank linked to electronic health records
Enables studies on genetic factors and personalized medicine approaches in liver disease
Canada
CARTaGENE[90 ]
Approximately 40,000
Genetic, clinical, environmental, and lifestyle data
Population-based biobank in Quebec focusing on chronic diseases
Provides data for research on environmental, nutrition, and genetic risk factors for
liver diseases
Canadian Partnership for Tomorrow's Health (CanPath)[91 ]
Over 300,000
Genetic, clinical, environmental, and lifestyle data
Pan-Canadian cohort integrating regional studies like CARTaGENE
Facilitates large-scale studies on liver disease risk factors across diverse Canadian
populations
Asia
China Kadoorie Biobank[92 ]
Approximately 500,000
Clinical and lifestyle data
Focus on chronic diseases in China
Data on common liver diseases in Asia and their risk factors
BioBank Japan[93 ]
Over 200,000
Genetic and clinical data
Focus on genetic data of Japanese individuals
Specific genetic data on liver disease in the Japanese population
To maximize the utility of these datasets, it is often necessary to systematically
integrate data from multiple sources, such as linking omics data with cohort studies
through a process known as data fusion.[12 ] This approach facilitates an integrative analysis of liver disease mechanisms, risk
factors, and treatment responses across different populations and healthcare systems.
A not yet existent uniform data structuring would allow for seamless integration and
interpretation, making insights more actionable for both researchers and clinicians.[13 ] With well-organized and standardized datasets, big data holds great promise for
improving clinical decision-making, improving diagnostic accuracy, enabling early
risk prediction, and guiding personalized interventions for liver diseases.
Opportunities in Big Data Analytics
Opportunities in Big Data Analytics
In clinical hepatology, big data and omics based technology facilitates real-world
evidence generation, enabling better stratification of patients and personalized medicine.[3 ] As disease entities in the field of hepatology are often heterogenous with a wide
range of clinical phenotypes associated with age of onset, course of disease, and
treatment responsiveness, analyzing big data can allow for refined subclassification
of diseases through a concept called “phenomapping,”[14 ] allowing clinicians to better understand the pathogenesis of disease entities as
well as develop predictive biomarkers/models. [Fig. 2 ] summarizes how big data analytics could be applied to different forms of omics based
data and its clinical applications described below.
Fig. 2 General schematic depicting examples of omics data and big data analytics.
Biomarkers, Predictions, and Precision Therapy
Big data analytics enables the identification of biomarkers that can serve as diagnostic,
prognostic, or therapeutic targets. The National Institute of Health identifies a
biomarker as an objective measure and indicator of biologic function, pathophysiological
processes, and response to treatment.[15 ] This has allowed for the application of big data in liver disease biomarker discovery.
Moreover, we want to emphasize how novel targeted therapies for MASLD (e.g., TR-β
agonists, GLP-1 receptor agonists, FGF21 analogs, and SGLT2 inhibitors) might leverage
biomarker databases to facilitate personalized treatment approaches.
For identification of steatosis in the Kadoorie Biobank glutathione disulfide and
diacylglycerol were found to be useful.[16 ] A study in UK Biobank has shown that lipidomic profiles, especially large HDL, may
differentiate metabolic dysfunction-associated steatotic liver disease (MASLD) and
combined metabolic dysfunction- and alcohol-related steatotic liver disease (MetALD).[17 ] As MASLD exhibits heterogeneity, a recent study identified two distinct phenotypic
clusters identified through partitioning around medoids clustering: a liver-specific
type with genetic links and rapid progression of chronic liver disease but limited
cardiovascular risk, and a cardiometabolic type associated with dysglycemia, hypertriglyceridemia,
and increased cardiovascular and diabetes risk. These clusters, validated across multiple
cohorts, demonstrated distinct liver transcriptomic signatures.[18 ] Moreover, 32 biomarkers, including SHGB and ApoB, were associated with an increased
incidence of HCC.[19 ] Plasma GDF15 was proposed to identify patients at risk of steatohepatitis. With
increased accessibility in accessing population-based health datasets, clinicians
and researchers can additionally check additional protein biomarkers associated with
liver disease in the new proteome-phenome atlas from the UK Biobank.[20 ] In addition, biomarker discovery can lead to the validation of newer targeted therapies
such as thyroid receptor β and FGF21 analogs.
Predictive models derived from big data are useful for stratifying patients based
on their disease progression and treatment responses. Combining clinical parameters
and sometimes omics data, these models can often predict outcomes such as fibrosis
progression, HCC risk, or mortality with greater accuracy than traditional clinical
scoring systems. Traditional liver injury markers such as transaminases as well as
intrinsic liver function laboratories including albumin and coagulation panels have
been critical in risk prediction of liver disease.[21 ] Traditional risk scores derived from large cohorts relevant in hepatology include
the Model for End-stage Liver Disease (MELD)[22 ] score, FIB-4 Index, AST/ALT ratio, and the Albumin-to-Platelet Ratio Index (APRI).[23 ] However, the availability of big data has allowed researchers and clinicians to
improve risk prediction with a combination of serum parameters and baseline demographics
including more novel scores such as the LiverRisk score[24 ] and the Chronic Liver Disease (CLivD) score.[25 ] In the general population, it is difficult to diagnose liver disease as the presentation
is generally asymptomatic. With the utilization of age, sex, alcohol intake, waist–hip
ratio (as opposed to BMI), diabetes, smoking, and GGT, the CLivD score was developed
to estimate 15-year risk of liver-related events.
Similarly in the study by Njei et al, the development of a machine learning model
with FAST scores, liver stiffness measurements, and aminotransferase levels outperformed
the ability to predict high-risk MASH compared with traditional FIB-4 or APRI scores.[26 ] For decompensation of cirrhosis the Early Prediction of Decompensation (EPOD) score
was developed in three population-based studies.[27 ] Liver cancer, one of the most common cancers in the world, poses a serious public
health burden due to high morbidity on diagnosis. Liu et al, using the UK Biobank
cohort, developed a risk prediction model for liver cancer in a subset of over 300,000
participants without previous diagnoses of cancer on the basis of sociodemographic
factors, physical measurements, lifestyle behaviors, and personal medical/family history.[28 ]
One of the most crucial developments in hepatology research has come from genome-wide
association studies (GWAS), uncovering the genetic basis of complex traits associated
with elevated liver enzymes[29 ] or steatotic liver disease.[30 ] As shown in [Table 1 ] many of the population-based studies include some type of genetic information, some
even whole genome sequencing.
One prime example of how genetic has been used to advance hepatology is MASLD: Understanding
genetic variants associated with MASLD has allowed us to better understand the functional
mechanism leading to hepatic steatosis, as these genes are often involved in hepatic
lipid metabolism, oxidative stress pathways, and inflammation leading to hepatic ballooning.[31 ]
[32 ] GWAS have promoted several genetic risk loci for MASLD by leveraging novel phenotypes
including ALT proxies and magnetic resonance imaging proton density fractionated fat
across various large cohorts.[30 ]
As GWAS are insightful at drawing various associations with genetic variants in large-scale
cohorts, a reverse methodology through phenome-wide association studies (PheWAS) and
genotype-first approaches has been employed to map variant of interest within a single
gene for multiple disease pathologies. In the setting of MASLD, this becomes relevant
as its fundamental pathophysiological mechanisms arise from insulin resistance and
dyslipidemia, thus leading to various phenotypic manifestations. Several genome-first
approaches have been employed for the study of hepatic steatosis, all of which incorporate
the data from large-scale biobanks with sometimes in vitro validation on cell models.[33 ]
[34 ]
[35 ]
[36 ] These advances highlight the importance of integrating genetic insights with clinical
and multi-omics data to refine our understanding of liver disease pathophysiology.
Although GWAS and PheWAS have significantly contributed to identifying risk loci and
elucidating disease mechanisms, future studies will need to incorporate functional
validation and mechanistic studies to translate these findings into clinical applications.
Notably, this genetic knowledge has already paved the way for the development of targeted
therapies, exemplified by the first siRNA therapies for PNPLA3 ,[37 ] the most well-known genetic variant associated with MASLD.[31 ]
Advancing Precision Medicine and Drug Repurposing
Advancing Precision Medicine and Drug Repurposing
Big data analytics facilitates the identification of patient subgroups that may benefit
from targeted therapies. In hepatology, this approach is particularly relevant for
tailoring treatments such as GLP-1 receptor agonists, immunotherapies, and antifibrotic
agents to specific patient profiles. By refining treatment strategies, precision medicine
can improve outcomes and reduce healthcare costs.
A key example of this progress is the rapid evolution of clinical trials leveraging
big data, which has recently culminated in the first FDA-approved treatment for advanced
fibrosis in MASH.[38 ] When starting this review in 2024, there were no FDA-approved treatment for MASH
despite these cohorts being at a higher risk of histologically proved liver fibrosis.
However, resmetirom, a liver-specific thyroid hormone receptor β-selective agonist,
was shown to achieve MASH resolution in almost 30% of patients who received 100 mg
of the medication as well as fibrosis improvement by at least one stage without worsening
of the MASLD activity score. In addition, there is an emerging evidence that fibroblast
growth factor 21 (FGF21) exhibits a substantial role in lipogenesis and hepatic insulin
sensitivity, which has led to an additive effect to lipid profiles in combination
with lifestyle changes such as diet.[39 ] As FGF21 also reflects liver fat accumulation, the exploration of transcriptomic
data of MASLD models in mice suggested that upregulated FGF21 levels were protective
against lipotoxicity and endoplasmic reticulum (ER) stress. As such, a phase 2b multicenter
clinical trial on the FGF21 analog pegozafermin has been shown to improve fibrosis
in biopsy-confirmed MASH.[40 ] New studies, which also include large cohorts, can now help pinpoint which patients
might benefit most from these new drugs.
The increasing integration of precision medicine in hepatology extends beyond novel
drug development to the potential repurposing of existing medications.[41 ] Given the metabolic constitution of MASLD, there is growing interest in leveraging
anti-diabetic agents to improve liver-related outcomes. As insulin resistance and
dyslipidemia coincide with the pathophysiology of hepatic steatosis, growing interest
has focused on the role of anti-diabetic agents in improving clinical outcomes for
these patients. With MASLD increasing in prevalence, institutions around the world
have used large-scale population databases to investigate how glucagon-like peptide
1 receptor agonists (GLP-1 RAs) could improve liver inflammation.[42 ] Multiple large-scale studies have suggested that GLP-1 RAs could be repurposed to
lower the risk of cirrhosis progression as well as reduce liver fat accumulation among
patients with concurrent MASLD and type 2 diabetes.[43 ]
[44 ] Sodium glucose transporter 2 (SGLT2) inhibitors have also been shown to reduce histological
steatosis as well as hepatic ballooning in MASLD likely by modulating inflammatory
pathways associated with interleukins and AMP-activated protein kinase/mammalian target
of rapamycin signaling.[45 ]
[46 ] Similarly, such mechanistic studies have allowed the study of SGLT2i in the context
of hepatocellular carcinoma and diabetes, which showed improvements in lipid profiles
and fibrosis.[47 ] However, there is not sufficient clinical evidence to suggest that SGLT2 inhibitors
could be effectively repurposed. As for dyslipidemia and cardiovascular disease, studies
in the UK Biobank as well as TriNetX cohort have suggested that statins, aspirin,
and omega-3 intake could reduce the incidence of liver disease as well as mortality
irrespective of genetic risk.[41 ]
[48 ]
[49 ]
Although targeted personalized therapies and drug repurposing hold promise, further
validation through well-powered clinical trials and mechanistic studies is essential.
Moreover, there is a new player that is poised to further revolutionize precision
hepatology.
Applications of AI in Big Data Analytics
Applications of AI in Big Data Analytics
The application of artificial intelligence (AI) is revolutionizing liver research,[50 ] especially in the analysis of extensive data from databases. AI technologies, such
as machine learning and deep learning, make it possible to detect complex patterns
in large datasets that are too complex for human analysts. By developing predictive
models, AI can improve the prediction of disease progression, treatment responses,
and potential risk factors.[51 ] AI tools can automate time-consuming tasks, such as data processing and analysis,
thus increasing research efficiency.[52 ] This is where the application of AI to population-based datasets is interesting,
with [Fig. 3 ] demonstrating a general schematic of different forms of data in hepatology that
could benefit from AI processing.
Fig. 3 Applying artificial intelligence to big data in hepatology.
Traditional statistical methods, such as regression models, have long served as foundational
tools in hepatology research. Regression analysis remains a cornerstone for identifying
associations between clinical variables, genetic risk factors, and disease outcomes,
providing interpretable insights into MASLD progression and treatment response. Building
on these statistical methods, machine learning techniques, including decision trees,
support vector machines, and neural networks, offer more sophisticated ways to model
complex interactions within large-scale datasets.[53 ] In a recent study by Yu et al, applications of deep learning algorithms such as
spatio-temporal 3D convolution networks demonstrated robustness in accurately diagnosing
HCC early based on triphasic CT liver scans.[54 ] Various methodologies stemming from AI models have allowed researchers to employ
novel parameters such as anthropometric and body composition indices to accurately
predict MASLD based on machine learning techniques applied to outcomes based on FibroScan
classifications of steatosis and fibrosis.[55 ]
A recent study identified how machine learning algorithms have been applied in hepatology:
Random forest classifications followed by decision trees and support vector machines
exhibited the highest average accuracy across 58 studies, with elevations in liver
enzymes (i.e., aminotransferases) being the most utilized feature to define the liver
phenotype.[56 ] Some examples of comparisons made between ML-based algorithms and conventional algorithms
include early detection and diagnosis of reversible stages of steatotic liver disease,
fibrosis risk in chronic hepatitis B, and development of hepatocellular carcinoma
in metabolic liver disease. In a study done across patients with chronic viral hepatitis
in Hong Kong, machine learning methods such as logistic regression, Adaboost, random
forest classifiers, and decision trees were comparatively employed to construct accurate
predictions of HCC in patients with chronic viral hepatitis based on already validated
risk scores such as the HCC ridge score.[57 ]
A comprehensive review paper by Ghosh et al well summarizes key areas that AI had
been applied to study a wide range of liver diseases.[58 ] As AI continues to evolve, its integration into hepatology promises to refine risk
stratification, enhance early diagnosis, and personalize treatment strategies for
MASLD and related liver diseases. Although traditional regression models remain essential
for hypothesis-driven research, advanced AI techniques not only offer the ability
to uncover hidden patterns and optimize decision-making, but also pose some challenges.
Challenges in Big Data for Hepatology
Challenges in Big Data for Hepatology
The increasing availability of diverse datasets and databases will revolutionize research
in hepatology. Not only does it allow new hypotheses to be generated, but it also
helps accelerate scientific discovery and innovation. However, challenges associated
with the use of these databases—such as data quality, ethical concerns, privacy, and
the need to distinguish between association and causality—must continue to be addressed
with care and integrity.
Data Quality and Heterogeneity
Although public databases offer a wealth of opportunities for liver research, there
are also several pitfalls that researchers should be aware of. These challenges can
influence the interpretation and use of the data and must be carefully considered
to achieve valid and reliable research results. Big data are often plagued by issues
such as missing data, variability in clinical measurements, and biases related to
population selection. These challenges can lead to misleading conclusions if not addressed
through robust data cleaning and normalization techniques. One of the biggest pitfalls
is the quality and completeness of the data. In some cases, records may have gaps,
be inconsistent, or contain errors. But also, the missing parameter in a cohort can
be useful information; therefore, imputation is hard and can lead to erroneous conclusions.
This can be especially the case if the data come from different sources or have been
collected over long periods of time. A critical assessment of data quality is crucial
to avoid erroneous conclusions. Another problem is the distortion of the data (bias).
For example, databases could contain a disproportionate amount of data from certain
demographic groups or regions, which can lead to a bias in research results. Moreover,
loss of follow-up has to be accounted for. Researchers need to consider these potential
biases and apply appropriate statistical methods to minimize them.[59 ] The results obtained from specific datasets may not be transferable to other populations
or contexts. This is particularly relevant when research data come from a limited
or special group of patients. Therefore, it is desirable to validate results in as
many diverse datasets as possible. Correctly interpreting large and complex datasets
also requires advanced statistical skills. Without proper methods and knowledge, researchers
can draw misleading conclusions or miss important patterns and connections. It is
also often underestimated that an advanced IT infrastructure is needed to carry out
analyses according to the current state of the art .
Computational and Technical Challenges
Despite promising research findings, the translation of big data insights into clinical
practice remains challenging. Factors such as a lack of interoperability between systems,
limited clinician training in data-driven tools, and resistance to change hinder the
implementation of big data in routine hepatology care. Analyzing large datasets requires
significant computational power. Scalability, efficient data storage, and the development
of algorithms capable of handling high-dimensional data are critical technical hurdles
in hepatology research. Similar to automated driving, the crucial question of responsibility
and liability in the context of a decision based on the integration of AI algorithms
in big data analytics is a point of contention. The challenges in hepatology are therefore
also to enable a safe and evidence-based implementation of AI to support clinical
decision-making. Various AI approaches have been used in hepatology, with a focus
on predicting events,[60 ] histological analyses,[61 ] and the prediction of treatment response.[62 ] The inability of AI algorithms to take into account information from the direct
interaction between patient and doctor is still an inherent limitation. AI algorithms
will therefore not yet be able to replace direct interaction between doctor and patient.
We see AI as a complementary tool to significantly improve patient–doctor interaction
and patient care in general.
Future Directions
Collaborative Networks and Open Data
The concept of Open Science plays a central role in transforming hepatology research.[63 ] By disclosing research data and results, scientists worldwide can access and use
the same resources to create high-quality and representative datasets.[64 ] It promotes the accessibility, transparency, and shareability of knowledge and data,
which often applies to the population-based datasets. Moreover, medical collaborative
networks aimed at sharing data sources from various different countries may provide
a streamlined process to collaborate on these datasets.[65 ] Peng et al, in a bibliometric and social network analysis, suggested that at present,
small academic groups are more popular in big data research with a broad range that
makes interaction between different groups sparse.[66 ] Examples of collaborative initiatives that have aimed to accelerate the sharing
of genomic and clinical data for the study of various disease processes include the
Global Alliance for Genomics and Health (GA4GH) which aims to distribute access to
central databases and allow researchers to reproduce their work by running established
methods over the same underlying data.[67 ] In the broader scheme, other additional open sources including the European Health
Data Evidence Network, the Observational Health Data Sciences and Informatics (ODHSI),
and All of Us have paved way for establishing international networks of researchers
with central databases of observational nature and establishing more streamlined pipelines
to analyze data. With the ODHSI data, researchers have been able to identify metabolic
risk factors in the cardiometabolic spectrum that may be relevant to patients with
fatty liver compared with alcohol-related fatty liver disease.[68 ] In the United States, the All of Us program maintains large-scale population information
to investigate various liver phenotypes, including drug-induced liver injury linked
to antibiotics.[69 ]
Hypotheses versus Big Data
The growing emphasis on data sharing and collaborative networks has not only expanded
access to biomedical datasets but has also influenced how research is conducted. Traditionally,
biomedical research has been dominated by hypothesis-driven approaches, where studies
were designed to test predefined mechanisms based on prior knowledge. However, the
advent of big data has shifted the paradigm toward data-driven discovery, allowing
researchers to uncover previously unknown patterns and relationships in hepatology
without a predefined hypothesis. This shift has sparked a debate: Should research
prioritize hypothesis testing or allow data to generate new hypotheses?
Big data approaches now enable the development of complex computational models integrating
multi-omics datasets.[70 ] These models facilitate network-based disease characterization, allowing for a systems
biology approach to understanding liver diseases. Rather than relying solely on predefined
mechanistic pathways, machine learning algorithms can reveal hidden biomarker signatures
and molecular interactions that may not have been considered in traditional hypothesis-driven
research. Despite the power of data-driven approaches, the debate remains unresolved—hypothesis-driven
research ensures biological interpretability and mechanistic understanding, while
data-driven methods offer unparalleled discovery potential. The future of hepatology
will likely depend on integrating both approaches, where data-driven insights inform
hypothesis generation, leading to targeted experimental validation and ultimately
improved clinical translation.
Focus on Implementation Science
Regardless of the approach, the ultimate goal remains the same: improving patient
care by translating research insights into clinical practice. This is where implementation
science plays a critical role. Although big data and AI have transformed our ability
to generate knowledge, their true impact depends on how effectively new findings,
risk models, and therapeutic strategies are integrated into healthcare systems. Implementation
science provides the framework to bridge this gap, ensuring that advances in hepatology
research lead to tangible improvements. Implementation science focuses on promoting
uptake of research into routine healthcare across clinical and organizational contexts.
By improving the adoption and implementation of strategies derived from large cohorts,
strategies must focus on identifying gaps in knowledge and practice to tailor interventions
for a specific target audience. One example in hepatology is the Veterans Administration's
National Hepatitis C Elimination Program, which successfully increased treatment rates
by strengthening strategies to facilitate the transition to direct-acting antivirals
from Year 1 to Year 2 while fostering stakeholder collaboration.[71(p2) ] Another example is the EASL-Lancet Commission on Liver Disease in Europe, which
has advocated for policy changes and public health interventions to address the growing
burden of liver disease.[72 ] Through a combination of data-driven decision-making and implementation strategies,
this initiative has helped shape guidelines for MASLD screening and prevention, emphasizing
the role of healthcare infrastructure in bridging the gap between research and practice.
But still implementation of results derived from large cohorts is not the standard.
Implementation, therefore, serves as a crucial bridge, ensuring that advances in precision
medicine, big data, and AI are integrated into real-world settings to improve patient
outcomes.
Conclusion
Usage of large population-based cohorts will only increase in the next years. However,
the true impact of these advancements depends on their successful implementation in
clinical practice. Collaborative networks, open data initiatives, replication studies,
and implementation science are essential to bridging the gap between large-scale discovery
and real-world application. Moving forward, the synergy between data-driven research,
clinical translation, and healthcare policy will be critical in transforming hepatology
and improving patient outcomes on a global scale.
Main Concepts and Learning Points
Main Concepts and Learning Points
Major concept
Key learning points
Role of large datasets
Big data and multi-omics in hepatology
- Integration of genetic, multi-omic, and clinical data increases liver disease understanding.
- Integration of structured and unstructured data still holds some challenges.
- Enable identification of disease patterns, risk factors, and biomarkers at a population
level.
- Facilitate longitudinal studies to track disease evolution and treatment responses.
Artificial intelligence in liver disease research
- AI enables the identification of patient subgroups and personalized risk stratification.
- Machine learning models help optimize treatment strategies and may uncover new therapeutic
targets.
- Provide high-dimensional data needed to train robust AI models.
- Improve predictive accuracy by capturing complex interactions between genetic, environmental,
and clinical factors.
Translational research and implementation science
- Bridging the gap between AI-driven discoveries and clinical applications is essential.
- Structured frameworks are needed to ensure real-world adoption of precision medicine.
- Large datasets support validation of AI models across diverse patient populations.
- Help assess generalizability and effectiveness of predictive models in real-world
settings.
Collaborative networks and open data initiatives
- Data sharing accelerates innovation and reproducibility.
- Cross-disciplinary collaboration increases the development of effective precision
medicine approaches.
- Enable meta-analyses and validation studies across different cohorts.
- Support the development of standardized protocols for data integration and analysis.
