This review aims to assess how AI- and ML-driven multi-omics offer comprehensive insights into pathogenicity, thereby enhancing diagnostic techniques and personalized therapeutic approaches in Crohn's disease and enhancing precision healthcare delivery.
Abstract
Crohn's disease is a long-term inflammatory disorder arising from the interaction of genetic risk factors, immune system dysfunction, and alterations in gut microbiota. Variability in clinical phenotypes and lack of biomarker specificity hinder the efficiency of current traditional diagnostic and treatment approaches. This review aims to assess how AI- and ML-driven multi-omics offer comprehensive insights into pathogenicity, thereby enhancing diagnostic techniques and personalized therapeutic approaches in CD. Current studies employ integration of multi-omics like genomics, proteomics, transcriptomics, metabolomics, and microbiome analysis in CD with AI and ML for significant advancement of biomarker discovery and clinical applications. Emerging evidence reveals that CD is a multi-factorial disorder involving host genetics, immune dysfunction, and microbiome shifts. Integration of advanced AI/ML models with multi-omics data can predict disease-specific biomarkers for easy diagnosis and facilitate precision medicine to enhance therapies. For a successful clinical implementation of an AI/ML model with multi-omics in CD, a standardized data framework and large-scale validation are needed. Additionally, future research should focus on developing interpretable AI models, real-time monitoring systems, and theranostic platforms to enhance precision healthcare delivery.
Microbiome-metabolome interactions are emerging as promising predictors of infectious disease, beyond conventional pathogen detection. Growing evidence shows that microbial dysbiosis, altered microbial-derived metabolites, and host metabolic reprogramming are associated with disease severity, immune dysfunction, treatment response, and mortality across infectious diseases. High-throughput sequencing, metagenomic next-generation sequencing, and nuclear magnetic resonance platforms have identified microbial and metabolic signatures that are prognostic for inflammatory activation, oxidative stress, mitochondrial dysfunction, and immune dysregulation. Integration of microbiome and metabolomic datasets with multi-omics frameworks may improve prognostic stratification compared to single-omics approaches. Artificial intelligence and machine-learning models, such as random forests, gradient boosting, and deep learning algorithms, have demonstrated promising potential for identifying high-dimensional prognostic patterns and aiding risk prediction. However, most of the available evidence remains exploratory and is hampered by cohort heterogeneity, small sample sizes, cross-sectional study designs, batch effects, limited external validation, and difficulties with model interpretability and reproducibility. Current evidence supports the potential of microbiome-metabolome biomarkers as complementary prognostic tools rather than routine clinical diagnostics. Future progress will require large, multicenter longitudinal studies, harmonized analytical frameworks, explainable artificial intelligence models, and equitable implementation strategies to enable clinically reliable precision infectious-disease prognostics.
S. Hudu, E. Morad, Ghusun M. Alhazimi et al.· Journal of the Formosan Medi...· 0 citations
The microbiome is increasingly recognized as a master regulator of immune homeostasis and a key environmental factor associated with the pathogenesis of autoimmune diseases (ADs). This review comprehensively synthesizes current knowledge on how microbial communities and their metabolites may contribute to ADs’ development through microbial-immune interactions, dysbiosis, and the involvement of viral and fungal components within an integrated inter-kingdom ecosystem. We propose an operational definition of microbiome biomarkers as measurable microbiome-associated features reflecting disease susceptibility, activity, prognosis, or therapeutic response and categorize them into three classes: taxonomic, functional/metabolic, and host–microbiome interaction-derived biomarkers. We critically evaluate the evidence for specific microbial signatures as biomarkers for early diagnosis, disease monitoring, and prediction of therapeutic responses, incorporating evidence grading that distinguishes validated biomarkers from those that remain exploratory and discussing shared versus disease-specific signatures across ADs. The translational potential of microbiome-targeted interventions, including probiotics, prebiotics, and fecal microbiota transplantation, is examined within a personalized medicine framework, with barriers to clinical implementation explicitly addressed. Key confounding factors such as diet, geographic origin, and medication use are highlighted as critical variables shaping microbiome signatures independently of disease. Looking forward, the convergence of multi-omics technologies and artificial intelligence for biomarker discovery, multi-omics integration, and clinical validation promises to unravel the complex microbiome-immune crosstalk, enabling more accurate diagnosis, prognostic stratification, and ultimately, individualized microbiota-informed therapy.
Qianqian He, Pinjun Zhang, Zhenni Chen et al.· Frontiers in Immunology· 1 citation
Type 1 diabetes (T1D) is a chronic metabolic disease mediated by autoimmunity. Its pathogenesis involves complex interactions between genetic susceptibility and environmental factors. Conventional T1D risk stratification primarily relies on genetic markers, islet autoantibodies, and glycemic indicators. Although these biomarkers remain indispensable in current clinical practice, they are often insufficient when used alone to accurately identify ultra-early high-risk individuals, predict disease progression rates, or support individualized preventive strategies. Consequently, more comprehensive molecular approaches are needed to improve precision risk stratification. In recent years, the rapid development of multi-omics technologies has provided new strategies for precise risk stratification of T1D. This narrative review critically evaluates how multi-omics integration strategies can improve precision risk stratification throughout the T1D disease continuum by integrating complementary molecular information from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and the microbiome. Particular emphasis is placed on stage-specific biomarker discovery, multi-omics data integration frameworks, artificial intelligence-assisted prediction models, biomarker validation, and the opportunities and challenges associated with clinical translation. Current evidence suggests that integrated multi-omics approaches have the potential to improve risk prediction accuracy, distinguish heterogeneous disease trajectories, identify individuals at imminent risk of progression, and provide biologically informed targets for precision intervention. However, important challenges remain, including data harmonization, external validation, model interpretability, cost-effectiveness, and integration into routine clinical screening programs. Future research should prioritize prospective multicenter cohorts, standardized analytical pipelines, externally validated prediction models, and clinically interpretable multi-omics frameworks to facilitate the translation of precision risk stratification into routine T1D prevention and management.
Ziyi Chen, Wen-Tao Liu, Zheng Guo et al.· Frontiers in Immunology· 0 citations
The human gut microbiome-host system represents a recently unleashed chemo-biological realm of crucial importance in human biology and health. So much so that new therapeutic approaches targeting it are emerging to prevent and treat a broad range of conditions, including inflammatory, metabolic, and cardiovascular diseases, infectious disorders, cancer, and neurodegeneration. From a drug discovery standpoint, this paradigm offers several distinctive advantages: it introduces novel therapeutic modalities (such as fecal microbiota transplantation, probiotics, prebiotics, and postbiotics), expands the biological search space to include the gut metagenome, unlocks new chemical space through microbial metabolites, and enables gut-localized pharmacokinetics with the potential to reduce systemic exposure and off-target effects. However, realizing this therapeutic potential critically depends on establishing causal links between specific microbiome features, microbial metabolites, and disease phenotypes. Achieving such causality requires the integration of diverse experimental and computational approaches across multiple scales, including epidemiological and clinical studies, metagenomics and longitudinal multi-omic profiling, gnotobiotic animal models, strain isolation and cultivation, biochemical and molecular analyses, and synthetic biology—supported by Artificial Intelligence, Bioinformatics, and Cheminformatics. In this Perspective, we provide a concise overview of this rapidly evolving field. We review the gut microbiome–host system and the principal tools used to interrogate it, with an emphasis on approaches that enable causality inference. We further evaluate current strategies for therapeutic intervention and conclude with an assessment of key achievements to date, as well as the major challenges and opportunities that will shape the future of microbiome-based drug discovery.
Gonzalo Colmenarejo· Frontiers in Drug Discovery· 0 citations
Sepsis is a life-threatening syndrome characterized by a heterogeneous host response to infection that remains a major cause of mortality worldwide. Current clinical scoring systems capture organ dysfunction but fail to reflect the underlying biological diversity, limiting their utility for patient stratification and targeted therapy. This review provides a comprehensive overview of molecular biomarker approaches used to predict sepsis course and prognosis in adult patients, covering genetic, transcriptomic, proteomic, and integrative strategies up to May 2026. Here, we summarize findings from genetic association studies, along with analyses based on polygenic risk scores to aggregate genetic effects, Mendelian randomization, and rare-variant sequencing approaches. We also review transcriptomic and proteomic strategies for endotyping, and diagnostic and prognostic discrimination. Lastly, we discuss how multi-omics integration is emerging as a promising framework to assist in distinguishing causal therapeutic targets from non-causal biomarkers. We also address the challenges that still constrain clinical translation towards precision medicine.
S. González-Barbuzano, E. Suárez-Pajés, M. Bardají-Carrillo et al.· Annals of Intensive Care· 0 citations
Autoimmune diseases are heterogeneous disorders caused by the interaction of inherited susceptibility, epigenetic remodeling, environmental exposure, and dysregulated innate and adaptive immunity. The rapid development of genome-wide association studies, next-generation sequencing, single-cell technologies, and high-throughput proteomics has expanded the spectrum of candidate biomarkers that can be used for risk prediction, early diagnosis, assessment of disease activity, prognosis, and treatment selection. This narrative review summarizes the clinical significance of HLA alleles, non-HLA susceptibility genes, polygenic risk scores, DNA methylation patterns, non-coding RNAs, autoantibodies, cytokines, complement components, and multi-omics signatures in major autoimmune diseases. Particular attention is given to the translation of biomarkers into personalized therapeutic strategies, including patient stratification for biologic agents, Janus kinase inhibitors, B-cell-targeted therapy, and cellular approaches. The main limitations remain population heterogeneity, insufficient external validation, pre-analytical variability, and the lack of standardized multi-marker panels. Integration of genetic, epigenetic, transcriptomic, proteomic, and clinical data is the most promising route toward reproducible biomarker systems and precision medicine in autoimmunity.
A. Akhmedov, Ramzan Khasainovich Tavsultanov, Anzhela Aslanbekovna Inazhaeva et al.· Genetics and Molecular Resea...· 0 citations
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