Clinical studies

Inflammatory bowel disease (IBD), encompassing ulcerative colitis and Crohn’s disease, is characterized by chronic gut inflammation driven by microbial dysbiosis and immune dysfunction. Current therapies primarily involve anti-inflammatory and immunomodulatory strategies; however, many patients experience an inadequate response or a gradual loss of efficacy over time. This study evaluates the clinical efficacy of personalized microbiome modulation (PMM)—an AI-driven intervention designed to restore microbial balance and improve key treatment outcomes such as symptom control and remission rates.

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Keywords: inflammatory bowel disease; microbiome; gut dysbiosis; personalized medicine; probiotics; biologic therapy; fecal calprotectin; inflammatory markers; AI-driven therapy

Inflammatory bowel disease (IBD) is a complex disorder influenced by genetic, environmental, and microbial factors, with emerging evidence highlighting the gut microbiome’s role in disease pathogenesis. This study investigates the impact of microbiome-targeted interventions in pediatric IBD by integrating multi-omics analysis, including metagenomics, metabolomics, transcriptomics, and clinical biomarkers, to identify microbial dysbiosis patterns and potential therapeutic targets. A cohort of pediatric IBD patients underwent a personalized intervention involving dietary modifications, probiotic supplementation, and selective antibiotic therapy.

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Keywords: pediatric inflammatory bowel disease (IBD); gut microbiome; dysbiosis; microbiome-targeted therapy; microbiome modulation; dietary intervention; probiotic supplementation

Inflammatory bowel disease (IBD) remains difficult to manage in patients who fail multiple therapeutic lines, and growing evidence suggests that alterations in the gut microbiome contribute to persistent symptoms and inflammatory activity. This study evaluated a three-month, AI-guided, multi-omic personalized microbiome modulation program in adults with treatment-refractory IBD. Baseline stool metagenomic sequencing, blood biomarkers, micronutrient panels, and clinical data were integrated through an artificial intelligence platform to generate individualized plans combining dietary adjustments, targeted synbiotics, selective antimicrobials, and micronutrient correction. Clinical outcomes, inflammatory markers, and microbial signatures were reassessed after three months. Across 358 participants, stool frequency decreased substantially, urgency and rectal bleeding resolved in most patients, and over 70% reported a “much improved” overall condition. Inflammatory biomarkers showed marked normalization, with reductions in hs-CRP and fecal calprotectin observed in over 85% of cases.

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Keywords: inflammatory bowel disease (IBD); multi-omics; gut microbiome modulation; artificial intelligence–guided therapy; immunometabolism; microbiome–immune interactions; personalized medicine; short-chain fatty acid–producing bacteria

Many cancer patients continue to experience long-lasting digestive problems such as diarrhea, bloating, and abdominal discomfort even after completing treatment. These symptoms are often managed with general measures that do not address the underlying cause, which may involve an imbalance in the gut microbiome. In this study, we evaluated a personalized approach that uses artificial intelligence to analyze each patient’s microbiome, clinical data, and laboratory results in order to generate individualized dietary and supplement recommendations. After three months, most patients showed clear improvement in bowel habits and overall energy levels. These clinical changes were accompanied by favorable changes in beneficial gut bacteria and related metabolic markers. Our findings suggest that personalized microbiome modulation may represent a promising strategy for improving long-term quality of life in cancer survivors and highlight the potential role of data-driven approaches in supportive oncology care.

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Keywords: cancer survivorship; gastrointestinal toxicity; gut microbiome; dysbiosis; personalized medicine; artificial intelligence; microbiome modulation; short-chain fatty acids; Bifidobacterium longum; precision supportive care