IBD, a rapidly growing gastrointestinal (GI) disease in Asia, presents with symptoms such as recurrent abdominal pain and diarrhea that often overlap with IBS, a common functional GI disorder.^2-4 Delayed or inaccurate diagnosis of IBD can lead to severe complications, including chronic inflammation, ulceration of the digestive tract, and, in advanced cases, surgical interventions.¹ Despite the widespread use of colonoscopy, histology, and imaging methods such as CT and MRI scans, a reliable non-invasive blood or stool test for IBD remains unavailable.^1,5 These challenges are compounded by the nonspecific nature of IBD symptoms, limited diagnostic tools, and a need to enhance clinicians' diagnostic skills, highlighting the urgent need for innovative diagnostic solutions.⁶

To address this gap, a multinational study analyzed 5,979 fecal samples from individuals with and without IBD, including ulcerative colitis (UC) and Crohn’s disease (CD).¹ The study utilized fecal metagenomics data from 4,406 samples across 13 cohorts in eight countries to identify and validate gut microbial biomarkers for IBD diagnosis.¹ Specifically, in-house sequencing data from Hong Kong was used as the discovery cohort, with validation performed using three additional in-house cohorts from Hong Kong and Australia, as well as nine public datasets from the United States, the Netherlands, mainland China, Spain, Denmark, and the United Kingdom.¹ The analysis identified 10 microbiota alterations uniquely associated with UC and nine linked to CD.¹

Patients with IBD exhibited significant differences in gut microbial communities at the phylum level compared to controls, including a reduction in Firmicutes and an enrichment of Proteobacteria. ¹ The reduced microbial diversity in IBD patients (median diversity: 2.73, CD: 2.71, controls: 3.08; p<0.001) and richness (median richness: UC: 82, CD: 86.5, controls: 95.0; p<0.001) further underscores the significant role of gut microbiota in disease progression.¹ Using the identified microbiota markers, machine-learning diagnostic models were developed to differentiate between UC and CD with exceptional accuracy.¹ The models achieved areas under the curve (AUC) exceeding 0.90 for both UC and CD, demonstrating their robustness in diagnosing IBD.¹ Importantly, these models outperformed traditional fecal calprotectin tests, offering greater sensitivity (72% vs. 54% for CD; 67% vs. 57% for UC) and specificity (95% vs. 86% for CD; 88% vs. 86% for UC) in detecting IBD.¹

To translate these findings into a practical clinical tool, a multiplex droplet digital polymerase chain reaction (m-ddPCR) test was developed to quantify bacterial biomarkers in fecal samples.¹ This test achieved high diagnostic performance, with an AUC of 0.88 (sensitivity: 85.0%, specificity: 81.8%) for UC and 0.87 (sensitivity: 90.2%, specificity: 76.0%) for CD in the discovery cohorts.¹ The m-ddPCR also showed strong correlations with metagenomic sequencing results, confirming its reliability.¹ Furthermore, the tool also showed promise in distinguishing active and inactive IBD cases, reflecting its potential applicability in monitoring disease activity and tailoring treatment strategies.¹ These findings were validated in independent cohorts, demonstrating their consistency across diverse populations and ethnicities, underscoring the potential for widespread clinical use.¹

In summary, the development of a microbiome-based diagnostic test addresses a critical unmet need for non-invasive, accurate differentiation between IBS and IBD.¹ By enabling timely intervention, this technology reduces delays in treatment, minimizes the need for invasive diagnostic procedures, and improves overall patient outcomes.^1,5,6