Emerging biomarkers are fundamental for the transition to precision medicine in IBD, aiming to enhance pathogenesis understanding, personalize therapies, and improve patient quality of life, establishing pathways for more effective, individualized management approaches.
Abstract
Background Inflammatory Bowel Diseases (IBD) are chronic conditions presenting significant diagnostic and management challenges. Current invasive methods and traditional biomarkers often lack sufficient accuracy and fail to address the disease’s heterogeneity and unpredictable therapeutic responses. This necessitates more precise, personalized clinical tools. Methods This scoping review synthesized recent findings on emerging biomarkers for IBD. We focused on technological advances in omics platforms (genomics, transcriptomics, proteomics, metabolomics, microbiomics), artificial intelligence, biosensors, and imaging techniques. Analysis identified biomarker potential for early diagnosis, disease activity monitoring, progression prognosis, and therapeutic response prediction. The review adhered to PRISMA-ScR guidelines and was registered with the Open Science Framework (OSF). The search encompassed five major databases: PubMed/MEDLINE, Scopus, Web of Science, Embase, and Google Scholar. Results Studies demonstrate vast potential in non-invasive biomarkers for refined early diagnosis, optimized disease monitoring, and treatment response prediction. Key findings include metabolomic and gut microbiota profiles, genetic and epigenetic markers, and AI integration of complex data. These approaches promise to overcome conventional indicator limitations. From 784 initial records, 27 articles were included, published between 2021 and 2025. Conclusion Emerging biomarkers are fundamental for the transition to precision medicine in IBD. Their implementation aims to enhance pathogenesis understanding, personalize therapies, and improve patient quality of life, establishing pathways for more effective, individualized management approaches.
Psoriasis vulgaris is a chronic immune-mediated inflammatory disease in which treatment response is assessed using clinical indices such as the Psoriasis Area and Severity Index (PASI) and Dermatology Life Quality Index (DLQI), despite their limited ability to capture systemic inflammation and underlying immunological activity. This narrative review aims to summarize current evidence on inflammatory biomarkers for monitoring treatment response and to evaluate their potential clinical utility. A structured, non-systematic literature search was performed in April–May 2026 across PubMed, Cochrane Library, Scopus, and ClinicalTrials.gov, focusing primarily on literature published during the preceding 10 years, with selected earlier studies being retained when directly relevant. Emerging data indicate that multiple biomarker domains may reflect therapeutic outcomes, including cytokines and chemokines, acute-phase proteins, complete-blood-count (CBC)-derived inflammatory indices, genetic markers, micro(mi)RNAs, metabolomic and lipidomic profiles, and tissue-based markers. These biomarkers may serve as severity-associated, baseline-predictive, pharmacodynamic, or prognostic markers, and these roles should not be interpreted interchangeably. Nevertheless, discrepancies between biomarker dynamics and clinical improvement occur, reflecting the partial dissociation between local and systemic inflammation, disease heterogeneity, and differences in response kinetics. Although inflammatory biomarkers provide a biologically grounded framework for assessing treatment response, their clinical implementation remains limited. Further large-scale, standardized studies are required to validate candidate markers and support the development of integrated, multi-omics approaches for personalized management.
J. Lewandowska, A. Owczarczyk-Saczonek, B. Nedoszytko· International Journal of Mol...· 0 citations
The present review examines the emerging role of germline human leukocyte antigen class I genotype as a prognostic and predictive biomarker, explicitly integrating it with tissue-based immune contexture and liquid biopsy readouts by proposing immunoediting as a unifying mechanistic framework that links allele-specific antigen presentation to immune infiltration.
Constantin N. Baxevanis, S. Stokidis, S. Fortis et al.· International Journal of Onc...· 0 citations
Clinicians are provided with a structured, evidence-based framework that identifies specific clinical scenarios where molecular diagnostics, host-response biomarkers, and three-dimensional imaging may meaningfully modify periodontal treatment decisions, supporting the operationalisation of precision approaches in daily practice.
S. Sevi, S. Romeggio, Gilberto Dinatale et al.· Clinical Oral Investigations· 0 citations
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.
Rheumatoid arthritis (RA) is a biologically heterogeneous immune-mediated disease characterized by substantial variability in therapeutic response. Despite the availability of multiple conventional synthetic, biologic, and targeted synthetic disease-modifying antirheumatic drugs (DMARDs), many patients fail to achieve adequate disease control or experience secondary loss of efficacy, underscoring the need for predictive biomarkers that can guide treatment selection. This narrative review was based on a structured literature search of PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar, covering publications from January 2000 to June 2026, with earlier landmark studies included when relevant. Literature selection followed PRISMA-informed principles, although the review was not designed as a formal systematic review. Unlike previous reviews that mainly catalogue RA biomarkers by analytical platform, drug class, or clinical use, this review integrates conventional and emerging biomarkers within a tissue-immunophenotype-centered framework. We critically evaluate clinical, serological, pharmacological, molecular, imaging, and tissue-based biomarkers according to biological plausibility, reproducibility, level of validation, clinical actionability, and translational readiness. Established markers such as rheumatoid factor, anti-citrullinated protein antibodies, acute-phase reactants, drug concentrations, and anti-drug antibodies remain clinically useful but provide incomplete insight into mechanism-specific therapeutic response. In contrast, synovial pathotypes, fibroblast and macrophage subsets, B-cell niches, tertiary lymphoid structures, single-cell and spatial omics, and ligand–receptor interaction networks offer a mechanistically richer view of treatment response and resistance. We conclude that precision medicine in RA will require integrated biomarker panels combining clinical, pharmacological, molecular, and synovial tissue data. The key future direction is the development of scalable, externally validated, and clinically interpretable models capable of assigning synovial endotypes and supporting mechanism-based therapeutic selection.
N. A. Batashkov, E. Gerasimova, D. Gerasimova et al.· Frontiers in Immunology· 0 citations
Current limitations in the development of consistent predictive biomarkers are outlined and future progress will require biomarker-stratified prospective trial designs with prespecified endpoints and rigorous external validation to establish clinically meaningful biomarkers.
Felix Lübbersmeyer, V. Schuettfort, Margit Fisch et al.· Current Opinion in Urology· 0 citations