Artificial intelligence-based personalized diet: A pilot clinical study for irritable bowel syndrome

ABSTRACT We enrolled consecutive IBS-M patients (n = 25) according to Rome IV criteria. Fecal samples were obtained from all patients twice (pre-and post-intervention) and high-throughput 16S rRNA sequencing was performed. Six weeks of personalized nutrition diet (n = 14) for group 1 and a standard IBS diet (n = 11) for group 2 were followed. AI-based diet was designed based on optimizing a personalized nutritional strategy by an algorithm regarding individual gut microbiome features. The IBS-SSS evaluation for pre- and post-intervention exhibited significant improvement (p < .02 and p < .001 for the standard IBS diet and personalized nutrition groups, respectively). While the IBS-SSS evaluation changed to moderate from severe in 78% (11 out of 14) of the personalized nutrition group, no such change was observed in the standard IBS diet group. A statistically significant increase in the Faecalibacterium genus was observed in the personalized nutrition group (p = .04). Bacteroides and putatively probiotic genus Propionibacterium were increased in the personalized nutrition group. The change (delta) values in IBS-SSS scores (before-after) in personalized nutrition and standard IBS diet groups are significantly higher in the personalized nutrition group. AI-based personalized microbiome modulation through diet significantly improves IBS-related symptoms in patients with IBS-M. Further large-scale, randomized placebo-controlled trials with long-term follow-up (durability) are needed. GRAPHICAL ABSTRACT

[1]  M. Fox,et al.  Low fermentable oligosaccharides, disaccharides, monosaccharides, and polyols diet compared with traditional dietary advice for diarrhea-predominant irritable bowel syndrome: a parallel-group, randomized controlled trial with analysis of clinical and microbiological factors associated with patient o , 2021, The American journal of clinical nutrition.

[2]  P. Kashyap,et al.  Functional Gastrointestinal Disorders and the Microbiome: What is the best strategy for moving microbiome-based therapies for functional gastrointestinal disorders into the clinic? , 2020, Gastroenterology.

[3]  Matthias Scholz,et al.  Gut microbiota associations with diet in irritable bowel syndrome and the effect of low FODMAP diet and probiotics. , 2020, Clinical nutrition.

[4]  E. Segal,et al.  The Gut Microbiome and Individual-Specific Responses to Diet , 2020, mSystems.

[5]  P. Gibson,et al.  Review article: FODMAPS, prebiotics and gut health‐the FODMAP hypothesis revisited , 2020, Alimentary pharmacology & therapeutics.

[6]  Theresa M. Hardy,et al.  Improved gastrointestinal health for irritable bowel syndrome with metagenome-guided interventions , 2020, Precision clinical medicine.

[7]  P. Whorwell,et al.  Worldwide Prevalence and Burden of Functional Gastrointestinal Disorders, Results of Rome Foundation Global Study. , 2020, Gastroenterology.

[8]  L. Engstrand,et al.  No distinct microbiome signature of irritable bowel syndrome found in a Swedish random population , 2019, Gut.

[9]  Takuya Akiba,et al.  Optuna: A Next-generation Hyperparameter Optimization Framework , 2019, KDD.

[10]  M. Surette,et al.  Gut Microbiota in Patients With Irritable Bowel Syndrome-A Systematic Review. , 2019, Gastroenterology.

[11]  M. Trauner,et al.  A Microbial Signature of Psychological Distress in Irritable Bowel Syndrome , 2018, Psychosomatic medicine.

[12]  Evgeni Levin,et al.  Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography , 2018, Nature Medicine.

[13]  Minhu Chen,et al.  Fecal Microbiota Alterations Associated With Diarrhea-Predominant Irritable Bowel Syndrome , 2018, Front. Microbiol..

[14]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[15]  L. Böhn,et al.  Diet low in FODMAPs reduces symptoms of irritable bowel syndrome as well as traditional dietary advice: a randomized controlled trial. , 2015, Gastroenterology.

[16]  H. Dupont.  Review article: evidence for the role of gut microbiota in irritable bowel syndrome and its potential influence on therapeutic targets , 2014, Alimentary pharmacology & therapeutics.

[17]  Björn Usadel,et al.  Trimmomatic: a flexible trimmer for Illumina sequence data , 2014, Bioinform..

[18]  Oleg Paliy,et al.  Quantitative Profiling of Gut Microbiota of Children With Diarrhea-Predominant Irritable Bowel Syndrome , 2012, The American Journal of Gastroenterology.

[19]  C. Consolandi,et al.  IBS-associated phylogenetic unbalances of the intestinal microbiota are not reverted by probiotic supplementation , 2012, Gut microbes.

[20]  Y. Ringel,et al.  Alterations in composition and diversity of the intestinal microbiota in patients with diarrhea‐predominant irritable bowel syndrome , 2012, Neurogastroenterology and motility : the official journal of the European Gastrointestinal Motility Society.

[21]  J. J. Abellán,et al.  Structural alterations of faecal and mucosa-associated bacterial communities in irritable bowel syndrome. , 2012, Environmental microbiology reports.

[22]  Eric P. Nawrocki,et al.  An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea , 2011, The ISME Journal.

[23]  Aleksandar Milosavljevic,et al.  Gastrointestinal microbiome signatures of pediatric patients with irritable bowel syndrome. , 2011, Gastroenterology.

[24]  S. Tims,et al.  Global and deep molecular analysis of microbiota signatures in fecal samples from patients with irritable bowel syndrome. , 2011, Gastroenterology.

[25]  Anne Salonen,et al.  Gastrointestinal microbiota in irritable bowel syndrome: present state and perspectives. , 2010, Microbiology.

[26]  William A. Walters,et al.  Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample , 2010, Proceedings of the National Academy of Sciences.

[27]  William A. Walters,et al.  QIIME allows analysis of high-throughput community sequencing data , 2010, Nature Methods.

[28]  Zhaohui Zheng,et al.  Stochastic gradient boosted distributed decision trees , 2009, CIKM.

[29]  J. Algina,et al.  Generalized eta and omega squared statistics: measures of effect size for some common research designs. , 2003, Psychological methods.

[30]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[31]  P. Gibson,et al.  Controversies and Recent Developments of the Low-FODMAP Diet. , 2017, Gastroenterology & hepatology.

[32]  Rémi Brazeilles,et al.  Identification of an Intestinal Microbiota Signature Associated With Severity of Irritable Bowel Syndrome. , 2017, Gastroenterology.

[33]  A. Spinelli.  Irritable Bowel Syndrome , 2007, Clinical drug investigation.

[34]  Francis Sullivan,et al.  The Metropolis Algorithm , 2000, Computing in Science & Engineering.