Skip to main content

Irritable bowel syndrome and microbiome; Switching from conventional diagnosis and therapies to personalized interventions

Abstract

The human microbiome has been linked to several diseases. Gastrointestinal diseases are still one of the most prominent area of study in host-microbiome interactions however the underlying microbial mechanisms in these disorders are not fully established. Irritable bowel syndrome (IBS) remains as one of the prominent disorders with significant changes in the gut microbiome composition and without definitive treatment. IBS has a severe impact on socio-economic and patient’s lifestyle. The association studies between the IBS and microbiome have shed a light on relevance of microbial composition, and hence microbiome-based trials were designed. However, there are no clear evidence of potential treatment for IBS. This review summarizes the epidemiology and socioeconomic impact of IBS and then focus on microbiome observational and clinical trials. At the end, we propose a new perspective on using data-driven approach and applying computational modelling and machine learning to design microbiome-aware personalized treatment for IBS.

Introduction

Irritable bowel syndrome (IBS) is one of the most common functional gastrointestinal disorder characterized by symptoms such as chronic recurrent abdominal pain, changes in stool consistency and frequency, changes in bowel habits, flatulence and bloating. IBS is currently diagnosed by symptomatic criteria, namely the Rome IV criteria, and sensitive and specific diagnostic markers are not established yet. According to the Rome IV criteria and based on predominant stool pattern, IBS patients are stratified into four main subtypes: IBS with diarrhea (IBS-D), IBS with constipation (IBS-C), IBS with mixed bowel habits (IBS-M), and unclassified IBS [1, 2].

IBS is believed to be a multifactorial and heterogeneous condition and its pathophysiology is not completely understood. Potential factors include genetic background, gut microbiome dysbiosis, dietary habits, psychological factors, and gastrointestinal infection [3, 4]. IBS shows a clear association with other gastrointestinal disorders, chronic pain disorders such as pelvic pain and fibromyalgia, and with psychiatric conditions such as depression, anxiety, and migraine [5, 6]. Patients with IBS and inflammatory bowel disease (IBD) may show similar symptoms, but while the pathogenesis of IBD involves mucosal inflammation, the pathogenesis of IBS is not clearly understood, and there is no causative biochemical or anatomical irregularity that can be used to diagnose IBS [7]. Despite great variety of therapeutic options, there has been no standard guideline or robust therapy for IBS, leading to suboptimal treatment satisfaction for both doctors and patients [8, 9]. Currently, there is no definitive cure for IBS, and relief of symptoms is what can be achieved by treatments.

Epidemics/Global and regional Prevalence

In 2012, a systemic review and meta-analysis covering 90 epidemiological studies across 33 countries worldwide reported a pooled global prevalence of IBS of 11.2% (95% CI: 9.8–12.8), varying widely from lowest ratio of 1.1% to highest rate of 45% between countries [10]. The origin of this variation is not clear. It may be mediated by factors such as diet, ethnicity and public health system, or might be resulted from methodological variations between studies. Gathering prevalence information of IBS subtypes is not straight forward as they show considerable overlap of symptoms and may switch over time. A couple of population studies in countries with pooled IBS prevalence of 10%, revealed IBS-D and IBS-C each contributes for approximately 30% of the diagnosed population [11, 12]. The IBS annual occurrence of new cases (IBS annual incidence rate) has not been reported for many countries, but a long-term population survey in the US shows an estimate in the range of 1–2% [13]. Worldwide analysis of IBS prevalence across 56 countries reported higher incidence rate in women than men (OR 1.67, 95% CI 1.53–1.82) [14].

Socio-economic impact and burden

IBS has substantial negative impact on patients’ personal and work life, and consequently on their family and society. Several health-related studies revealed consistent reduction in quality of life (QOL) of IBS patients in European and North American populations [15, 16]. Reluctance to leave home and avoidance of social places was reported mainly in IBS-D patients, whereas difficulty in concentration and avoiding sex was more likely to be seen in IBS-C patients. IBS also has a negative effect on work life including less tendency to travel, reduced socializing and loss of earning. Overall, individuals with IBS report unpredictability of their symptoms and highlight that they can feel stigmatized by family and friends, who might struggle to understand the impact of IBS on their quality of life [17, 18]. In fact, patients with severe symptoms show more tendency to accept high level of risk to resolve their symptoms. For example, a questionnaire study reported that people with severe IBS are willing to give up an average of 15 years (up to 25%) of their remaining life expectancy to live free of symptoms [19].

Recently, Shorey et. al. published a qualitative systematic review about IBS patient’s perspectives on healthcare, daily living, and self-care management. They analyzed 17 studies including 299 adults that diagnosed with IBS, aged between 19 and 88 years and majority from Europe and North America. They identified four themes: (1) physical, psychological, and social impacts; (2) effects on work life; (3) handling IBS; and (4) relevant support with required sources. They also mentioned importance of the integrating technology to design IBS-related support systems to enhance patients' health literacy, countering societal stigma against IBS, and evaluating the effectiveness of the social networks to support adults living with IBS [20].

IBS brings substantial direct and indirect costs to patients and society. In 2013, a systemic review of the economic burden of IBS analyzing 35 studies, estimated a considerable health care cost in USA, ranging from $1562 to $ 7547 per patient per year [21]. Similarly, analysis covering data from six European countries estimated per capita cost of 1183 € to 3358 € [22]. Also, economic impact of IBS on the health care system has been estimated to be up to $ 2 billion per annum in China [23]. IBS has considerable indirect costs like other chronic conditions such as migraine and asthma, due to loss of work performance and productivity. A study encompassing data from 13 European countries estimated annual indirect cost of 2314 € per-capita for IBS (Fig. 1) [22].

Fig. 1
figure 1

Irritable Bowel Syndrome (IBS). Global and regional prevalence [10], socio-economic burden [24] and comorbidity [6]

Pathophysiology and risk factors

The pathophysiology of IBS is largely undetermined and current understanding of the potential underlying mechanisms is incomplete. However, cumulative knowledge and growing evidence during the past decades suggest contribution of the gut microbiota, bile acids, food antigens and the intestinal epithelial barrier in producing anomalous responses in the main regulators of the sensory-motor functions in IBS, including immune system, the enteric nervous system (ENS), the hypothalamus–pituitary–adrenal (HPA) axis and the gut-brain axis [6, 24,25,26]. In addition, psychosocial factors such as stress, that influences physiological functions of the gut, and factors such as anxiety and depression, which are known to be influenced by gastrointestinal symptoms, have been acknowledged in pathophysiology of the IBS. Some investigators have reported familial aggregation of IBS and findings from twin studies have shown higher concordance in monozygotic twins compared with dizygotic twins, suggesting potential underlying genetic factors in IBS [27,28,29]. A recently published genome-wide association study including 53,400 people with IBS and 433,201 controls, identified six independent genetic susceptibility loci for IBS at genome-wide significance (P < 5 × 10 − 8) and all six loci were replicated at Bonferroni significance (P < 0.0083) using data from an independent panel from 23andMe (205,252 cases and 1,384,055 controls). This study reported strong genome-wide association between IBS and mood and anxiety disorders rooted to shared pathogenic pathways [30]. Due to the important role of serotonin in the brain–gut axis, genetics of serotonergic pathways, especially the serotonin transporter (SERT), have gained a great amount of attention in recent years [31]. In a meta-analysis covering more than 7,000 participants across 27 studies, authors reported significant association between SERT insertion or deletion polymorphism and the risk of IBS [32]. Female gender is a well-documented risk factor for IBS, with an average odds ratio of 1.67 across population-based studies [14]. A genome-wide association study comparing data from 9,576 IBS patients and 336,449 healthy controls in UK biobank, identified an association between IBS risk in women only and variants at a locus on chromosome 9, which might support the rationale for studying the role of sex hormones in the pathophysiology of the functional gastrointestinal disorders [33]. The corticotropin-releasing hormone (CRH) is vital to the body’s stress response and studies in Japanese subjects have identified association between single nucleotide polymorphism in genes encoding CRH receptors 1&2 and IBS symptoms, indicating possible role of the CRH pathway in IBS pathophysiology [34, 35]. Several studies have shown an association between previous bacterial or viral gastrointestinal infections and risk of developing post-infectious IBS (PI-IBS) [36, 37]. A range of bacterial pathogens have been implicated in PI-IBS, including Clostridioides difficile1 [38], Vibrio cholerae [39], Campylobacter jejuni, Escherichia coli and Salmonella enterica serovar Typhimurium [40].

Some dietary compounds might be involved in the development and progress of IBS symptoms. More than 25 years ago, it was reported that the consumption of large amounts of insoluble fiber intensifies symptoms in IBS patients [41]. Some subgroups of IBS patients experience exacerbated symptoms when consuming food containing fermentable oligosaccharides, monosaccharides, disaccharides and polyols [42]. Removal of gluten from the diet has a positive effect on a proportion of IBS patients by improving symptoms [43]. There is evidence for a role of disordered bile acid metabolism in IBS pathophysiology. Cross-sectional surveys by 23-seleno 25-homotaurocholic acid retention scanning revealed that approximately 20% of IBS patients with diarrhea show indication of idiopathic bile acid diarrhea. Also, an investigation of the association between fecal bile acids and IBS symptoms, revealed that total fecal bile acids concentration was lower in IBC-C and higher in IBC-D subtypes [6, 44]. Figure 2 summarizes risk factors, pathophysiological mechanisms, and genetics findings associated with IBS.

Fig. 2
figure 2

Potential interconnected factors that regulate the manifestation of IBS symptoms. IBS has a multifactorial pathophysiology and multiple interrelated pathways can influence the manifestation of symptoms. External factors are dominant, but internal factors such as gut microbiome, gastrointestinal immune system and genetic makeup is also likely to be crucial for the development and progression of symptoms. Here we summarized potential external and internal factors and genetic findings linked to underlying pathophysiological mechanisms of IBS [6, 24, 26, 30, 96]. HPA, hypothalamic–pituitary–adrenal axis; ADRA, adrenoceptor-α; aINS, anterior insula; CDC42, cell division cycle 42; CDH1, cadherin 1; CGN, cingulin; CLDN, claudin; COMT, catechol-O-methyltransferase; CRHR1, corticotropin-releasing hormone receptor 1; FGFR4, fibroblast growth factor receptor 4; GLUL, glutamate-ammonia ligase; GPBAR1, G protein-coupled bile acid receptor 1; GRID2IP, GRID2-interacting protein; HTR, 5-hydroxytryptamine receptor; IL, interleukin; KLB, Klotho-β; mir, microRNA; NKRF, nuclear factor-κB-repressing factor; SCN5A, sodium voltage-gated channel α-subunit 5; SLC6A4, solute carrier family 6 member 4; TNF, tumour necrosis factor; TNFSF15, TNF superfamily member 15; TRPV1, transient receptor potential cation channel subfamily V member 1;; NCAM1, Neural Cell Adhesion Molecule 1; CADM2, Cell Adhesion Molecule 2; PHF2, PHD Finger Protein 2; DOCK9, Dedicator Of Cytokinesis 9

IBS and Microbiome

A growing body of evidence indicates that the gut microbiota plays an important role in gastrointestinal (GI) disease including IBS. The fecal microbiota of IBS patients differs significantly from healthy subjects, with potential contribution to altered bowel habits and influencing colonic transit [45, 46]. Several studies have indicated that the abundance of Bifidobacterium, Lactobacillus and Faecalibacterium is reduced, while the abundance of Veillonella, Ruminococcus and proinflammatory bacterial species such as Enterobacteriaceae is increased [47,48,49,50,51]. Conversely, a recent systematic review of gut microbiota in patient with IBS reported increased abundance of family Lactobacillaceae and genus Bacteroides [52]. Both higher and lower ratio of Firmicutes/Bacteroidetes, that is a rough indicator of altered microbial population, has been reported in IBS subjects [53, 54]. Reduced diversity of gut microbiome and presence of Clostridiales, Prevotella and methanogenic species has been proposed as an IBS-specific microbiome signature that associate with severity of symptoms [55]. However, this microbial signature cannot yet be explicitly correlated nor explained by application of medicines, differences in dietary habits or genetic factors. The gut metabolome, intestinal permeability and inflammatory pathways have also been suggested to play a role microbiome-related background of gastrointestinal disease [56] (Fig. 3).

Fig. 3
figure 3

IBS-Microbiome-Diet axis. The gut microbiome might be an important factor with higher degrees of dysbiosis and altered abundance of some species observed in IBS patients. Diet might have a substantial effect on IBS symptoms through mechanisms, such as changing gut microbiota, direct effect of food, and immune activation. Fermentable oligosaccharides, disaccharides, monosaccharides and polyols (FODMAPs) might cause IBS symptoms via microbiome dysbiosis, bacterial fermentation and osmotic overload [97]. Gut microbiota composition and function is shaped by several factors from which, diet might be the key determinant of the microbiota configuration. Oral microbiome may have a potential in diagnosis and patient stratification in IBS. LPS, lipopolysaccharide

These findings indicate potential influence of the gut microbiota in the development of effective treatments for IBS. To better understand the role of the gut microbiome in the IBS pathology, it is important to explore the inter-species and host-microbe interactions, as well as the interplay between microbiome composition and factors that influence the IBS severity, such as sex and psychiatric comorbidities.

Therapeutic Interventions

The purpose of most current therapeutic interventions is to reduce visceral pain and/or to change predominant problematic bowel habits in IBS. However, an emerging field is manipulation of the gut microbiota. Table 1 provides a summary of the current approved medications for treatment of IBS-related symptoms.

Table 1 Summary of treatments for IBS-related symptoms [98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115]

Pain in IBS is partially the result of the smooth muscle spasm, and antispasmodic drugs such as neurokinin-type 2 receptor antagonists and calcium channel blockers are used as the first-line treatment in pain-predominant IBS patients. IBS is associated with psychological disorders and low-dose antidepressants, such as selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs), are recommended for the treatment of pain in patients. Simple laxatives, such as docusate and senna, are often effective first line therapy in patients diagnosed with IBS-C, followed by liaclotide as second line therapy. Antidiarrhoeals, such as μ‑opioid receptor agonist loperamide, are used to prolong the gastrointestinal transit time and to improve diarrhea in patients with IBS-D [6, 57].

Although it is not clear whether alterations in the gut microbiota in IBS patients precede or are an outcome of the disrupted local gut microenvironment condition, modulation of gut microbiota for treatment of the IBS has sparked interest in recent years. Several facts support this tendency: some pre-/probiotics can relieve IBS symptoms [58]; visceral colonic hypersensitivity that is a critical feature of the IBS can be transferred to germ-free mice by fecal transplantation [59]; randomized controlled trails with rifaximin, a no-absorbable antibiotic, revealed benefit for IBS patients [60]; a systematic review and meta-analysis including 45 studies and 21,421 individuals with infectious enteritis, has reported fourfold higher risk of developing IBS in individuals with gastrointestinal infection [61]; dietary interventions that are known to modulate gut microbiota, such as a diet low in FODMAPs (Fermentable Oligo-, Di-, Mono-saccharides And Polyols), have also reported to reduce symptoms of IBS in several randomized placebo-controlled trials [62,63,64,65,66,67]; recent observations suggest a positive effect of fecal microbiota transplantation in alleviating IBS symptoms [68, 69]. Overall, there is growing evidence supporting microbiome-based therapeutic approaches for the treatment of IBS. Table 2 summarizes a couple of recent systematic reviews and meta-analysis that evaluated the effect of probiotics, prebiotics, dietary intervention, non-absorbable antibiotics, and FMTs for the treatment of IBS.

Table 2 Systematic reviews with meta-analysis reporting efficacy of microbiome-based therapeutic interventions in IBS

Host-microbiome and diet interactions

Employing personalized nutrition to modulate host-microbiota interactions is a new therapeutic avenue for disease prevention and control. The role of host genetics in shaping the composition of gut microbiota has been reported in some studies, but environmental factors overweighed the genetics [70,71,72]. While early life events have significant impact on the gut microbiota, it does maintain some degree of elasticity and can be shaped by later environmental factors, such as diet, hygiene, antibiotic and non-antibiotic drugs, weather, pollution, and so forth. Of these, diet is the key driving force that modulate abundance and functions of microbial species, in a fast, personalized, and reproducible manner [73,74,75]. Collectively, interdependent function of the three biologically and chemically interconnected systems defines an individual’s response to dietary interventions: host physiology and metabolism; the microbiota structure and state; and diet composition and timing [76, 77].

Variations in dietary macronutrients, including carbohydrates, protein and fat, significantly changes gut microbiota. Carbohydrates, dependent on their types and amounts, have complex effect on the gut microbiota. It has been reported that long-term consumption of complex carbohydrates promotes Prevotella. Dietary fibers influences microbiome ecology and elevates abundance of Bacteroidetes [78, 79]. Some bacteria can grow on specific types of carbohydrates, and consequently diet can eliminate or select for certain species. For example, grain-reduced diet can decrease abundance of Bifidobacteria that selectively degrade arabinoxylans in grains [80]. Despite common signature of response to carbohydrates within population, highly personalized shifts have been reported in response to dietary fibers, carbohydrate containing prebiotics and resistant starches [81,82,83,84]. In humans, short-term administration of diets rich in animal-protein resulted in decreased abundance of saccharolytic species (including Ruminococcus bromii, Eubacterium rectale and Roseburia spp.), while increased abundance of bile-tolerant species, such as Bacteroides, Bilophila and Alistipes [74]. Moreover, consumption of diet rich in plant protein resulted in elevated production of short-chain fatty acid (SCFA) and considerable increased abundance of commensals Bifidobacteria and Lactobacilli [85]. Also, Long term consumption of the animal protein diet has been associated with the Bacteroides [78] (Fig. 3).

Exponential increase of microbiome data and need for predictive models

Big biological datasets contain the raw data required to gain insights into complex biological systems, but high-level analysis is needed to realize the potential of these data. Machine learning is a discipline in computational science where computers are trained to learn patterns from data. Machine learning methods aim to recognize patterns and to develop predictive models based on statistical associations between features from a given dataset. The machine learning algorithm typically consist of the measurement across a set of samples which are called features, and the labels that model aims to predict using features. The learning process, that is based on a set of mathematical assumptions and rules, refers to finding the optimal set of parameters that translate the features in the training dataset into correct predictions of the labels in the test dataset. In life science, features can cover one or more types of data, such as a genomic sequence, gene expression profiles, protein expression levels, protein–protein interactions, metabolite concentrations, abundance profiles or copy number alterations. Labels can be binary e.g. pathogenic or non-pathogenic, continuous e.g. growth rate, or categorical e.g., stage of disease [86, 87]. Machine learning methods can be split into two main categories: unsupervised and supervised learning. Supervised approaches are used when the labels on the input data are available. Several types of supervised algorithms exist, including linear methods, decision trees, neural networks, and support vector machines. Unsupervised learning is applied when labels are unknown for the input date. Clustering and principal component analysis (PCA) are frequently used unsupervised methods [88].

Nowadays, machine learning technology has been applied to almost every field of science and engineering on a global scale. Life science and healthcare have widely benefitted from machine learning and powerful algorithms are now available to diagnose disease, stratify patients, develop drugs, repurpose drugs, predict treatment outcomes, and recommend personalized treatment [89]. The past few years have seen an upsurge in the application of machine learning within microbiome research, following the publication of large accessible datasets such as The NIH Human Microbiome Project [90]. A variety of machine learning approaches, such as logistic regression, neural networks, and support vector machines, have been used to identify microbial features present in several disease states [91].

In a study by Zeevi et al., the authors successfully predicted post-meal glycemic response by training a regression model based on the individual’s microbiome features and personal information together with their diet’s nutrient profile [75]. A similar approach was employed in a later study to show that personalized glycemic response to different bread types can be predicted based on prior microbiome data [92]. In a recently published landmark study, authors reported a trained deep neural network model that could predict antibiotics based on structure. They applied the model on multiple chemical libraries and discovered a novel molecule with antibacterial effect [93].

The entire machine learning process is highly reliant on the quality of the input data and can be affected by factors including the implementation of the algorithms, definition of the parameters, and selection of the features. Projects using machine learning for microbiome studies and microbiome therapeutics will probably require information on microbiota, drugs, host metabolism and host-microbiota interactions. Table 3 provides a summary list of the representative databases with potential for application of the machine learning in microbiome field.

Table 3 List of the representative databases with potential for application of the machine learning in microbiome field

Perspective: Integration of Modeling and Machine Learning to design microbiome-aware personalized treatment

During the past decade, the gut microbiome has emerged as a biological system with high therapeutic potential, and advances in our understanding of the microbiome and its interaction with the host have opened a new horizon in biotechnology and precision medicine. There is strong evidence supporting the role of diet and microbiome in the triggering and progression of IBS, and targeting microbiota appears promising considering positive response of some patients to microbiome-related therapies. However, the complexity and heterogeneity of IBS and lack of highly predictive diagnostic and prognostic biomarkers resulted in unsatisfactory outcomes.

Progress in high-throughput technologies and bioinformatics has facilitated the acquisition of multi-dimensional clinical and biological data and the translation of these data into knowledge. Several studies have demonstrated the capacity to collect comprehensive, longitudinal datasets for individuals, including quantification of intestinal and dietary metabolite concentrations, classification and characterization of the host data (including diet, anthropometrics, lifestyle and disease background) and microbiome data (such as strain-level composition and abundance, metagenomics, meta-transcriptomics and metabolomics). However, most current studies involving interactions between human physiology, microbiome and food remain correlative rather than explanatory. A deeper understanding of the underlying mechanisms is important in designing safe and efficient novel therapeutic interventions, such as pre/probiotics, synbiotics, antibiotics and dietary regimes/food supplements.

Detecting the potentially interfering factors with efficacy of the synbiotics and dietary compounds and exploring underlying mechanisms, will require the development of algorithms that integrate multi-scale data and suggest the optimal combinations that would result in desired beneficial transformations. Recent studies tried to provide mechanistic insight by reconstructing genome-scale metabolic model of gut species and using these models to simulate host-microbiome-diet interactions [94, 95]. Despite their promise, limited coverage and low accuracy of the reconstructed metabolic models are major challenges for translation of these approaches. Moreover, there is currently no efficient approach to perform temporospatial simulation of species-level metabolic interactions.

Ultimately, these advances will enable the development of in silico platforms that can integrate high dimensional data and provide mechanistic insight into host, microbiome, and diet interaction. Developed computational platform can integrate multi-dimensional datasets and provide a structured, curated and simulation ready database that allows for the implementation of the desired features, machine learning algorithms and predictive multiscale models (Fig. 4). Multiscale modeling can detect underlying mechanistic chain and causal mechanisms of disorders, complementing machine learning techniques that are agnostic to causality. Personalized models can be reconstructed based on measured variants for disease process in an individual patient and combined with machine learning, to create a personalized in silico pair of the physical condition. Developed platform can be employed for better characterization of the disorder and for identification of the potential therapies prior to clinical trials. Such in silico platforms have the potential to drive a paradigm shift in prevention, diagnosis, and treatment of the diseases in a personalize manner.

Fig. 4
figure 4

Microbiome-aware in silico platform. Schematic representation of the data driven platform that integrates multiscale modeling and artificial intelligence to provider deeper mechanistic understanding of microbiome and host response. This platform defines a dysbiosis fingerprint using person-specific data and employs algorithms to design precision diet/synbiotics to transfer this dysbiosis fingerprint towards symbiotic fingerprint. This platform can be used to formulate and to produce new generation of the optimally designed food supplements and pre/probiotics to improve desired trait for individuals or stratified populations

Availability of data and materials

There is no new data generated as part of this review.

References

  1. Drossman DA, Hasler WL. Rome IV-Functional GI Disorders: Disorders of Gut-Brain Interaction. Gastroenterology. 2016;150:1257–61. https://doi.org/10.1053/j.gastro.2016.03.035.

    Article  PubMed  Google Scholar 

  2. Simren M, Palsson OS, Whitehead WE. Update on Rome IV Criteria for Colorectal Disorders: Implications for Clinical Practice. Curr Gastroenterol Rep. 2017;19:15. https://doi.org/10.1007/s11894-017-0554-0.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Barbara G, et al. The Intestinal Microenvironment and Functional Gastrointestinal Disorders. Gastroenterology. 2016. https://doi.org/10.1053/j.gastro.2016.02.028.

    Article  PubMed  Google Scholar 

  4. Mari, A., Abu Baker, F., Mahamid, M., Sbeit, W. & Khoury, T. The Evolving Role of Gut Microbiota in the Management of Irritable Bowel Syndrome: An Overview of the Current Knowledge. J Clin Med 9, doi:https://doi.org/10.3390/jcm9030685 (2020).

  5. Ford AC, Lacy BE, Talley NJ. Irritable Bowel Syndrome. N Engl J Med. 2017;376:2566–78. https://doi.org/10.1056/NEJMra1607547.

    CAS  Article  PubMed  Google Scholar 

  6. Enck P, et al. Irritable bowel syndrome. Nat Rev Dis Primers. 2016;2:16014. https://doi.org/10.1038/nrdp.2016.14.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Gibson, P. R., Varney, J., Malakar, S. & Muir, J. G. Food components and irritable bowel syndrome. Gastroenterology 148, 1158–1174 e1154, doi:https://doi.org/10.1053/j.gastro.2015.02.005 (2015).

  8. Moayyedi P, et al. Irritable bowel syndrome diagnosis and management: A simplified algorithm for clinical practice. United European Gastroenterol J. 2017;5:773–88. https://doi.org/10.1177/2050640617731968.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lacy BE. Diagnosis and treatment of diarrhea-predominant irritable bowel syndrome. Int J Gen Med. 2016;9:7–17. https://doi.org/10.2147/IJGM.S93698.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Lovell, R. M. & Ford, A. C. Global prevalence of and risk factors for irritable bowel syndrome: a meta-analysis. Clin Gastroenterol Hepatol 10, 712–721 e714, doi:https://doi.org/10.1016/j.cgh.2012.02.029 (2012).

  11. Hungin AP, Whorwell PJ, Tack J, Mearin F. The prevalence, patterns and impact of irritable bowel syndrome: an international survey of 40,000 subjects. Aliment Pharmacol Ther. 2003;17:643–50. https://doi.org/10.1046/j.1365-2036.2003.01456.x.

    CAS  Article  PubMed  Google Scholar 

  12. Olafsdottir LB, Gudjonsson H, Jonsdottir HH, Thjodleifsson B. Stability of the irritable bowel syndrome and subgroups as measured by three diagnostic criteria - a 10-year follow-up study. Aliment Pharmacol Ther. 2010;32:670–80. https://doi.org/10.1111/j.1365-2036.2010.04388.x.

    CAS  Article  PubMed  Google Scholar 

  13. Halder SL, et al. Natural history of functional gastrointestinal disorders: a 12-year longitudinal population-based study. Gastroenterology. 2007;133:799–807. https://doi.org/10.1053/j.gastro.2007.06.010.

    Article  PubMed  Google Scholar 

  14. Lovell RM, Ford AC. Effect of gender on prevalence of irritable bowel syndrome in the community: systematic review and meta-analysis. Am J Gastroenterol. 2012;107:991–1000. https://doi.org/10.1038/ajg.2012.131.

    Article  PubMed  Google Scholar 

  15. Frank, L. et al. Health-related quality of life associated with irritable bowel syndrome: comparison with other chronic diseases. Clin Ther 24, 675–689; discussion 674, doi:https://doi.org/10.1016/s0149-2918(02)85143-8 (2002).

  16. Canavan C, West J, Card T. Review article: the economic impact of the irritable bowel syndrome. Aliment Pharmacol Ther. 2014;40:1023–34. https://doi.org/10.1111/apt.12938.

    CAS  Article  PubMed  Google Scholar 

  17. Bushnell DM, Martin ML, Ricci JF, Bracco A. Performance of the EQ-5D in patients with irritable bowel syndrome. Value Health. 2006;9:90–7. https://doi.org/10.1111/j.1524-4733.2006.00086.x.

    Article  PubMed  Google Scholar 

  18. Ballou, S. & Keefer, L. The impact of irritable bowel syndrome on daily functioning: Characterizing and understanding daily consequences of IBS. Neurogastroenterol Motil 29, doi:https://doi.org/10.1111/nmo.12982 (2017).

  19. Drossman DA, et al. International survey of patients with IBS: symptom features and their severity, health status, treatments, and risk taking to achieve clinical benefit. J Clin Gastroenterol. 2009;43:541–50. https://doi.org/10.1097/MCG.0b013e318189a7f9.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Shorey S, Demutska A, Chan V, Siah KTH. Adults living with irritable bowel syndrome (IBS): A qualitative systematic review. J Psychosom Res. 2021;140: 110289. https://doi.org/10.1016/j.jpsychores.2020.110289.

    Article  PubMed  Google Scholar 

  21. Nellesen D, Yee K, Chawla A, Lewis BE, Carson RT. A systematic review of the economic and humanistic burden of illness in irritable bowel syndrome and chronic constipation. J Manag Care Pharm. 2013;19:755–64. https://doi.org/10.18553/jmcp.2013.19.9.755.

    Article  PubMed  Google Scholar 

  22. Flacco ME, et al. Costs of irritable bowel syndrome in European countries with universal healthcare coverage: a meta-analysis. Eur Rev Med Pharmacol Sci. 2019;23:2986–3000. https://doi.org/10.26355/eurrev_201904_17580.

    CAS  Article  PubMed  Google Scholar 

  23. Zhang F, Xiang W, Li CY, Li SC. Economic burden of irritable bowel syndrome in China. World J Gastroenterol. 2016;22:10450–60. https://doi.org/10.3748/wjg.v22.i47.10450.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Black CJ, Ford AC. Global burden of irritable bowel syndrome: trends, predictions and risk factors. Nat Rev Gastroenterol Hepatol. 2020;17:473–86. https://doi.org/10.1038/s41575-020-0286-8.

    Article  PubMed  Google Scholar 

  25. Chey WD, Kurlander J, Eswaran S. Irritable bowel syndrome: a clinical review. JAMA. 2015;313:949–58. https://doi.org/10.1001/jama.2015.0954.

    CAS  Article  PubMed  Google Scholar 

  26. Holtmann GJ, Ford AC, Talley NJ. Pathophysiology of irritable bowel syndrome. Lancet Gastroenterol Hepatol. 2016;1:133–46. https://doi.org/10.1016/S2468-1253(16)30023-1.

    Article  PubMed  Google Scholar 

  27. Saito YA, et al. Familial aggregation of irritable bowel syndrome: a family case-control study. Am J Gastroenterol. 2010;105:833–41. https://doi.org/10.1038/ajg.2010.116.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Waehrens R, Ohlsson H, Sundquist J, Sundquist K, Zoller B. Risk of irritable bowel syndrome in first-degree, second-degree and third-degree relatives of affected individuals: a nationwide family study in Sweden. Gut. 2015;64:215–21. https://doi.org/10.1136/gutjnl-2013-305705.

    Article  PubMed  Google Scholar 

  29. Bengtson MB, Ronning T, Vatn MH, Harris JR. Irritable bowel syndrome in twins: genes and environment. Gut. 2006;55:1754–9. https://doi.org/10.1136/gut.2006.097287.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Eijsbouts C, et al. Genome-wide analysis of 53,400 people with irritable bowel syndrome highlights shared genetic pathways with mood and anxiety disorders. Nat Genet. 2021;53:1543–52. https://doi.org/10.1038/s41588-021-00950-8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. Jin DC, et al. Regulation of the serotonin transporter in the pathogenesis of irritable bowel syndrome. World J Gastroenterol. 2016;22:8137–48. https://doi.org/10.3748/wjg.v22.i36.8137.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. Zhu Y, Zheng G, Hu Z. Association between SERT insertion/deletion polymorphism and the risk of irritable bowel syndrome: A meta-analysis based on 7039 subjects. Gene. 2018;679:133–7. https://doi.org/10.1016/j.gene.2018.08.059.

    CAS  Article  PubMed  Google Scholar 

  33. Bonfiglio F, et al. Female-Specific Association Between Variants on Chromosome 9 and Self-Reported Diagnosis of Irritable Bowel Syndrome. Gastroenterology. 2018;155:168–79. https://doi.org/10.1053/j.gastro.2018.03.064.

    CAS  Article  PubMed  Google Scholar 

  34. Komuro H, et al. Corticotropin-Releasing Hormone Receptor 2 Gene Variants in Irritable Bowel Syndrome. PLoS ONE. 2016;11: e0147817. https://doi.org/10.1371/journal.pone.0147817.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. Sato N, et al. Corticotropin-releasing hormone receptor 1 gene variants in irritable bowel syndrome. PLoS ONE. 2012;7: e42450. https://doi.org/10.1371/journal.pone.0042450.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. Schwille-Kiuntke J, Mazurak N, Enck P. Systematic review with meta-analysis: post-infectious irritable bowel syndrome after travellers’ diarrhoea. Aliment Pharmacol Ther. 2015;41:1029–37. https://doi.org/10.1111/apt.13199.

    CAS  Article  PubMed  Google Scholar 

  37. Spiller R, Garsed K. Postinfectious irritable bowel syndrome. Gastroenterology. 2009;136:1979–88. https://doi.org/10.1053/j.gastro.2009.02.074.

    Article  PubMed  Google Scholar 

  38. Wadhwa A, et al. High risk of post-infectious irritable bowel syndrome in patients with Clostridium difficile infection. Aliment Pharmacol Ther. 2016;44:576–82. https://doi.org/10.1111/apt.13737.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. Ghoshal UC, Rahman MM. Post-infection irritable bowel syndrome in the tropical and subtropical regions: Vibrio cholerae is a new cause of this well-known condition. Indian J Gastroenterol. 2019;38:87–94. https://doi.org/10.1007/s12664-019-00959-2.

    Article  PubMed  Google Scholar 

  40. Thabane M, Kottachchi DT, Marshall JK. Systematic review and meta-analysis: The incidence and prognosis of post-infectious irritable bowel syndrome. Aliment Pharmacol Ther. 2007;26:535–44. https://doi.org/10.1111/j.1365-2036.2007.03399.x.

    CAS  Article  PubMed  Google Scholar 

  41. Francis CY, Whorwell PJ. Bran and irritable bowel syndrome: time for reappraisal. Lancet. 1994;344:39–40. https://doi.org/10.1016/s0140-6736(94)91055-3.

    CAS  Article  PubMed  Google Scholar 

  42. Shepherd SJ, Parker FC, Muir JG, Gibson PR. Dietary triggers of abdominal symptoms in patients with irritable bowel syndrome: randomized placebo-controlled evidence. Clin Gastroenterol Hepatol. 2008;6:765–71. https://doi.org/10.1016/j.cgh.2008.02.058.

    CAS  Article  PubMed  Google Scholar 

  43. Elli L, et al. Evidence for the Presence of Non-Celiac Gluten Sensitivity in Patients with Functional Gastrointestinal Symptoms: Results from a Multicenter Randomized Double-Blind Placebo-Controlled Gluten Challenge. Nutrients. 2016;8:84. https://doi.org/10.3390/nu8020084.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. Shin, A. et al. Bowel functions, fecal unconjugated primary and secondary bile acids, and colonic transit in patients with irritable bowel syndrome. Clin Gastroenterol Hepatol 11, 1270–1275 e1271, doi:https://doi.org/10.1016/j.cgh.2013.04.020 (2013).

  45. Parthasarathy, G. et al. Relationship Between Microbiota of the Colonic Mucosa vs Feces and Symptoms, Colonic Transit, and Methane Production in Female Patients With Chronic Constipation. Gastroenterology 150, 367–379 e361, doi:https://doi.org/10.1053/j.gastro.2015.10.005 (2016).

  46. Kassinen A, et al. The fecal microbiota of irritable bowel syndrome patients differs significantly from that of healthy subjects. Gastroenterology. 2007;133:24–33. https://doi.org/10.1053/j.gastro.2007.04.005.

    CAS  Article  PubMed  Google Scholar 

  47. Zhuang X, Xiong L, Li L, Li M, Chen M. Alterations of gut microbiota in patients with irritable bowel syndrome: A systematic review and meta-analysis. J Gastroenterol Hepatol. 2017;32:28–38. https://doi.org/10.1111/jgh.13471.

    Article  PubMed  Google Scholar 

  48. Carroll IM, Chang YH, Park J, Sartor RB, Ringel Y. Luminal and mucosal-associated intestinal microbiota in patients with diarrhea-predominant irritable bowel syndrome. Gut Pathog. 2010;2:19. https://doi.org/10.1186/1757-4749-2-19.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Kerckhoffs AP, et al. Lower Bifidobacteria counts in both duodenal mucosa-associated and fecal microbiota in irritable bowel syndrome patients. World J Gastroenterol. 2009;15:2887–92. https://doi.org/10.3748/wjg.15.2887.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Rajilic-Stojanovic M, et al. Global and deep molecular analysis of microbiota signatures in fecal samples from patients with irritable bowel syndrome. Gastroenterology. 2011;141:1792–801. https://doi.org/10.1053/j.gastro.2011.07.043.

    CAS  Article  PubMed  Google Scholar 

  51. Malinen E, et al. Analysis of the fecal microbiota of irritable bowel syndrome patients and healthy controls with real-time PCR. Am J Gastroenterol. 2005;100:373–82. https://doi.org/10.1111/j.1572-0241.2005.40312.x.

    CAS  Article  PubMed  Google Scholar 

  52. Pittayanon R, et al. Gut Microbiota in Patients With Irritable Bowel Syndrome-A Systematic Review. Gastroenterology. 2019;157:97–108. https://doi.org/10.1053/j.gastro.2019.03.049.

    Article  PubMed  Google Scholar 

  53. Jalanka-Tuovinen J, et al. Faecal microbiota composition and host-microbe cross-talk following gastroenteritis and in postinfectious irritable bowel syndrome. Gut. 2014;63:1737–45. https://doi.org/10.1136/gutjnl-2013-305994.

    Article  PubMed  Google Scholar 

  54. Jeffery IB, et al. An irritable bowel syndrome subtype defined by species-specific alterations in faecal microbiota. Gut. 2012;61:997–1006. https://doi.org/10.1136/gutjnl-2011-301501.

    Article  PubMed  Google Scholar 

  55. Tap, J. et al. Identification of an Intestinal Microbiota Signature Associated With Severity of Irritable Bowel Syndrome. Gastroenterology 152, 111–123 e118, doi:https://doi.org/10.1053/j.gastro.2016.09.049 (2017).

  56. Quigley EMM. Gut microbiome as a clinical tool in gastrointestinal disease management: are we there yet? Nat Rev Gastroenterol Hepatol. 2017;14:315–20. https://doi.org/10.1038/nrgastro.2017.29.

    Article  PubMed  Google Scholar 

  57. Xu D, et al. Efficacy of fecal microbiota transplantation in irritable bowel syndrome: a systematic review and meta-analysis. Am J Gastroenterol. 2019;114:1043–50. https://doi.org/10.14309/ajg.0000000000000198.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Ford, A. C. et al. Efficacy of prebiotics, probiotics, and synbiotics in irritable bowel syndrome and chronic idiopathic constipation: systematic review and meta-analysis. Am J Gastroenterol 109, 1547–1561; quiz 1546, 1562, doi:https://doi.org/10.1038/ajg.2014.202 (2014).

  59. Crouzet L, et al. The hypersensitivity to colonic distension of IBS patients can be transferred to rats through their fecal microbiota. Neurogastroenterol Motil. 2013;25:e272-282. https://doi.org/10.1111/nmo.12103.

    CAS  Article  PubMed  Google Scholar 

  60. Pimentel M, et al. Rifaximin therapy for patients with irritable bowel syndrome without constipation. New Engl J Med. 2011;364:22–32. https://doi.org/10.1056/NEJMoa1004409.

    CAS  Article  PubMed  Google Scholar 

  61. Klem F, et al. Prevalence, risk factors, and outcomes of irritable bowel syndrome after infectious enteritis: a systematic review and meta-analysis. Gastroenterology. 2017;152:1042. https://doi.org/10.1053/j.gastro.2016.12.039.

    Article  PubMed  Google Scholar 

  62. Halmos EP, Power VA, Shepherd SJ, Gibson PR, Muir JG. A Diet Low in FODMAPs Reduces Symptoms of Irritable Bowel Syndrome. Gastroenterology. 2014;146:67. https://doi.org/10.1053/j.gastro.2013.09.046.

    CAS  Article  PubMed  Google Scholar 

  63. Staudacher HM, et al. Fermentable carbohydrate restriction reduces luminal bifidobacteria and gastrointestinal symptoms in patients with irritable bowel syndrome. J Nutr. 2012;142:1510–8. https://doi.org/10.3945/jn.112.159285.

    CAS  Article  PubMed  Google Scholar 

  64. Bohn L, et al. Diet Low in FODMAPs reduces symptoms of irritable bowel syndrome as well as traditional dietary advice: a randomized controlled trial. Gastroenterology. 2015;149:1399. https://doi.org/10.1053/j.gastro.2015.07.054.

    CAS  Article  PubMed  Google Scholar 

  65. Gibson PR, Varney J, Malakar S, Muir JG. Food components and irritable bowel syndrome. Gastroenterology. 2015;148:1158-U1111. https://doi.org/10.1053/j.gastro.2015.02.005.

    Article  PubMed  Google Scholar 

  66. Altomare A, et al. Diarrhea Predominant-Irritable Bowel Syndrome (IBS-D): Effects of Different Nutritional Patterns on Intestinal Dysbiosis and Symptoms. Nutrients. 2021. https://doi.org/10.3390/nu13051506.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Linsalata M, et al. The Relationship between Low Serum Vitamin D Levels and Altered Intestinal Barrier Function in Patients with IBS Diarrhoea Undergoing a Long-Term Low-FODMAP Diet: Novel Observations from a Clinical Trial. Nutrients. 2021. https://doi.org/10.3390/nu13031011.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Xu DB, et al. Efficacy of Fecal Microbiota Transplantation in Irritable Bowel Syndrome: A Systematic Review and Meta-Analysis. American Journal of Gastroenterology. 2019;114:1043–50. https://doi.org/10.14309/ajg.0000000000000198.

    Article  Google Scholar 

  69. Ianiro G, et al. Systematic review with meta-analysis: efficacy of faecal microbiota transplantation for the treatment of irritable bowel syndrome. Aliment Pharm Ther. 2019;50:240–8. https://doi.org/10.1111/apt.15330.

    Article  Google Scholar 

  70. Goodrich JK, et al. Human Genetics Shape the Gut Microbiome. Cell. 2014;159:789–99. https://doi.org/10.1016/j.cell.2014.09.053.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  71. Rothschild D, et al. Environment dominates over host genetics in shaping human gut microbiota. Nature. 2018;555:210. https://doi.org/10.1038/nature25973.

    CAS  Article  PubMed  Google Scholar 

  72. Yatsunenko T, et al. Human gut microbiome viewed across age and geography. Nature. 2012;486:222. https://doi.org/10.1038/nature11053.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  73. Claesson MJ, et al. Gut microbiota composition correlates with diet and health in the elderly. Nature. 2012;488:178. https://doi.org/10.1038/nature11319.

    CAS  Article  PubMed  Google Scholar 

  74. David LA, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505:559. https://doi.org/10.1038/nature12820.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  75. Zeevi D, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163:1079–94. https://doi.org/10.1016/j.cell.2015.11.001.

    CAS  Article  PubMed  Google Scholar 

  76. Kau AL, Ahern PP, Griffin NW, Goodman AL, Gordon JI. Human nutrition, the gut microbiome and the immune system. Nature. 2011;474:327–36. https://doi.org/10.1038/nature10213.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  77. Zmora N, Suez J, Elinav E. You are what you eat: diet, health and the gut microbiota. Nat Rev Gastro Hepat. 2019;16:35–56. https://doi.org/10.1038/s41575-018-0061-2.

    CAS  Article  Google Scholar 

  78. Wu GD, et al. Linking long-term dietary patterns with gut microbial enterotypes. Science. 2011;334:105–8. https://doi.org/10.1126/science.1208344.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  79. De Filippo C, et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci USA. 2010;107:14691–6. https://doi.org/10.1073/pnas.1005963107.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Hansen LBS, et al. A low-gluten diet induces changes in the intestinal microbiome of healthy Danish adults. Nat Commun. 2018;9:4630. https://doi.org/10.1038/s41467-018-07019-x.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  81. Walker AW, et al. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. Isme J. 2011;5:220–30. https://doi.org/10.1038/Ismej.2010.118.

    CAS  Article  PubMed  Google Scholar 

  82. Tap J, et al. Gut microbiota richness promotes its stability upon increased dietary fibre intake in healthy adults. Environ Microbiol. 2015;17:4954–64. https://doi.org/10.1111/1462-2920.13006.

    CAS  Article  PubMed  Google Scholar 

  83. Martinez I, Kim J, Duffy PR, Schlegel VL, Walter J. Resistant Starches types 2 and 4 have differential effects on the composition of the fecal microbiota in human subjects. PLoS ONE. 2010;5:e15046. https://doi.org/10.1371/journal.pone.0015046.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  84. Davis LMG, Martinez I, Walter J, Goin C, Hutkins RW. Barcoded Pyrosequencing Reveals That Consumption of Galactooligosaccharides Results in a Highly Specific Bifidogenic Response in Humans. PLoS ONE. 2011;6:e25200. https://doi.org/10.1371/journal.pone.0025200.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  85. Swiatecka D, Narbad A, Ridgway KP, Kostyra H. The study on the impact of glycated pea proteins on human intestinal bacteria (vol 145, pg 267, 2011). Int J Food Microbiol. 2011;151:340–340. https://doi.org/10.1016/j.ijfoodmicro.2011.08.017.

    Article  Google Scholar 

  86. Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ. Next-generation machine learning for biological networks. Cell. 2018;173:1581–92. https://doi.org/10.1016/j.cell.2018.05.015.

    CAS  Article  PubMed  Google Scholar 

  87. Cammarota G, et al. Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nat Rev Gastro Hepat. 2020;17:635–48. https://doi.org/10.1038/s41575-020-0327-3.

    Article  Google Scholar 

  88. James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning with applications in r introduction. Springer Texts Stat. 2013;103:1–14. https://doi.org/10.1007/978-1-4614-7138-7_1.

    Article  Google Scholar 

  89. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthcare J. 2019;6:94–8. https://doi.org/10.7861/futurehosp.6-2-94.

    Article  Google Scholar 

  90. Peterson J, et al. The NIH Human Microbiome Project. Genome Res. 2009;19:2317–23. https://doi.org/10.1101/gr.096651.109.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  91. Pasolli E, Truong DT, Malik F, Waldron L, Segata N. Machine learning meta-analysis of large metagenomic datasets: tools and biological insights. Plos Comput Biol. 2016;12: e1004977. https://doi.org/10.1371/journal.pcbi.1004977.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  92. Korem T, et al. Bread Affects Clinical Parameters and Induces Gut Microbiome-Associated Personal Glycemic Responses. Cell Metab. 2017;25:1243. https://doi.org/10.1016/j.cmet.2017.05.002.

    CAS  Article  PubMed  Google Scholar 

  93. Stokes JM, et al. A deep learning approach to antibiotic discovery. Cell. 2020;180:688. https://doi.org/10.1016/j.cell.2020.01.021.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  94. Shoaie S, et al. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab. 2015;22:320–31. https://doi.org/10.1016/j.cmet.2015.07.001.

    CAS  Article  PubMed  Google Scholar 

  95. Bauer E, Thiele I. From metagenomic data to personalized in silico microbiotas: predicting dietary supplements for Crohn’s disease. Npj Syst Biol Appl. 2018;4:27. https://doi.org/10.1038/s41540-018-0063-2.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Gazouli M, et al. Lessons learned–resolving the enigma of genetic factors in IBS. Nat Rev Gastroenterol Hepatol. 2016;13:77–87. https://doi.org/10.1038/nrgastro.2015.206.

    CAS  Article  PubMed  Google Scholar 

  97. Moayyedi P, Simren M, Bercik P. Evidence-based and mechanistic insights into exclusion diets for IBS. Nat Rev Gastro Hepat. 2020;17:406–13. https://doi.org/10.1038/s41575-020-0270-3.

    CAS  Article  Google Scholar 

  98. Luthra P, et al. Efficacy of drugs in chronic idiopathic constipation: a systematic review and network meta-analysis. Lancet Gastroenterol Hepatol. 2019;4:831–44. https://doi.org/10.1016/S2468-1253(19)30246-8.

    Article  PubMed  Google Scholar 

  99. Black CJ, Burr NE, Ford AC. Relative efficacy of tegaserod in a systematic review and network meta-analysis of licensed therapies for irritable bowel syndrome with constipation. Clin Gastroenterol Hepatol. 2020;18:1238–9. https://doi.org/10.1016/j.cgh.2019.07.007.

    Article  PubMed  Google Scholar 

  100. Chey WD, Lembo AJ, Rosenbaum DP. Efficacy of tenapanor in treating patients with irritable bowel syndrome with constipation: a 12-week, placebo-controlled phase 3 trial (T3MPO-1). Am J Gastroenterol. 2020;115:281–93. https://doi.org/10.14309/ajg.0000000000000516.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Nakajima A, et al. Safety and efficacy of elobixibat for chronic constipation: results from a randomised, double-blind, placebo-controlled, phase 3 trial and an open-label, single-arm, phase 3 trial. Lancet Gastroenterol Hepatol. 2018;3:537–47. https://doi.org/10.1016/S2468-1253(18)30123-7.

    Article  PubMed  Google Scholar 

  102. Brenner DM, et al. Efficacy, safety, and tolerability of plecanatide in patients with irritable bowel syndrome with constipation: results of two phase 3 randomized clinical trials. Am J Gastroenterol. 2018;113:735–45. https://doi.org/10.1038/s41395-018-0026-7.

    CAS  Article  PubMed  Google Scholar 

  103. Barish CF, Griffin P. Safety and tolerability of plecanatide in patients with chronic idiopathic constipation: long-term evidence from an open-label study. Curr Med Res Opin. 2018;34:751–5. https://doi.org/10.1080/03007995.2018.1430024.

    CAS  Article  PubMed  Google Scholar 

  104. Chapman RW, Stanghellini V, Geraint M, Halphen M. Randomized clinical trial: macrogol/PEG 3350 plus electrolytes for treatment of patients with constipation associated with irritable bowel syndrome. Am J Gastroenterol. 2013;108:1508–15. https://doi.org/10.1038/ajg.2013.197.

    CAS  Article  PubMed  Google Scholar 

  105. Chey WD, et al. Safety and patient outcomes with lubiprostone for up to 52 weeks in patients with irritable bowel syndrome with constipation. Aliment Pharmacol Ther. 2012;35:587–99. https://doi.org/10.1111/j.1365-2036.2011.04983.x.

    CAS  Article  PubMed  Google Scholar 

  106. Vijayvargiya P, et al. Effects of colesevelam on bowel symptoms, biomarkers, and colonic mucosal gene expression in patients with bile acid diarrhea in a randomized trial. Clin Gastroenterol Hepatol. 2020;18:2962–70. https://doi.org/10.1016/j.cgh.2020.02.027.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  107. Bajor A, Tornblom H, Rudling M, Ung KA, Simren M. Increased colonic bile acid exposure: a relevant factor for symptoms and treatment in IBS. Gut. 2015;64:84–92. https://doi.org/10.1136/gutjnl-2013-305965.

    CAS  Article  PubMed  Google Scholar 

  108. Black CJ, et al. Efficacy of pharmacological therapies in patients with IBS with diarrhoea or mixed stool pattern: systematic review and network meta-analysis. Gut. 2020;69:74–82. https://doi.org/10.1136/gutjnl-2018-318160.

    CAS  Article  PubMed  Google Scholar 

  109. Andresen V, et al. Effects of 5-hydroxytryptamine (serotonin) type 3 antagonists on symptom relief and constipation in nonconstipated irritable bowel syndrome: a systematic review and meta-analysis of randomized controlled trials. Clin Gastroenterol Hepatol. 2008;6:545–55. https://doi.org/10.1016/j.cgh.2007.12.015.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  110. Menees SB, Maneerattannaporn M, Kim HM, Chey WD. The efficacy and safety of rifaximin for the irritable bowel syndrome: a systematic review and meta-analysis. Am J Gastroenterol. 2012;107:28–35. https://doi.org/10.1038/ajg.2011.355.

    CAS  Article  PubMed  Google Scholar 

  111. Black CJ, et al. Efficacy of soluble fibre, antispasmodic drugs, and gut-brain neuromodulators in irritable bowel syndrome: a systematic review and network meta-analysis. Lancet Gastroenterol Hepatol. 2020;5:117–31. https://doi.org/10.1016/S2468-1253(19)30324-3.

    Article  PubMed  Google Scholar 

  112. Khanna R, MacDonald JK, Levesque BG. Peppermint oil for the treatment of irritable bowel syndrome: a systematic review and meta-analysis. J Clin Gastroenterol. 2014;48:505–12. https://doi.org/10.1097/MCG.0b013e3182a88357.

    Article  PubMed  Google Scholar 

  113. Ford AC, et al. Effect of antidepressants and psychological therapies, including hypnotherapy, in irritable bowel syndrome: systematic review and meta-analysis. Am J Gastroenterol. 2014;109:1350–65. https://doi.org/10.1038/ajg.2014.148.

    CAS  Article  PubMed  Google Scholar 

  114. Quartero AO, Meineche-Schmidt V, Muris J, Rubin G, de Wit N. Bulking agents, antispasmodic and antidepressant medication for the treatment of irritable bowel syndrome. Cochrane Database Syst Rev. 2005;1:CD003460. https://doi.org/10.1002/14651858.CD003460.pub2.

    Article  Google Scholar 

  115. Ford AC, et al. Effect of fibre, antispasmodics, and peppermint oil in the treatment of irritable bowel syndrome: systematic review and meta-analysis. BMJ. 2008;337: a2313. https://doi.org/10.1136/bmj.a2313.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the entire team at Nielsen lab and Shoaie lab for the suggestions and discussions to draft this review.

Funding

PG and LKN were supported by the Novo Nordisk Foundation Grants NNF20CC0035580 and NNF14OC0009473. SS was supported by Engineering and Physical Sciences Research Council (EPSRC), EP/S001301/1, Biotechnology Biological Sciences Research Council (BBSRC) BB/S016899/1 and Science for Life Laboratory.

Author information

Affiliations

Authors

Contributions

PG performed literature review and drafted paper. PG, SS, LKN read, revised, and approved final draft. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Saeed Shoaie or Lars K. Nielsen.

Ethics declarations

Ethics approval and consent to participate

There is no new data generated as part of this review.

Consent for publication

All the authors read the final version and approved it.

Competing interests

There is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ghaffari, P., Shoaie, S. & Nielsen, L.K. Irritable bowel syndrome and microbiome; Switching from conventional diagnosis and therapies to personalized interventions. J Transl Med 20, 173 (2022). https://doi.org/10.1186/s12967-022-03365-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12967-022-03365-z