Impact of dietary fiber on gut microbiota composition, function and gut-brain-modules in healthy adults – a systematic review protocol

Introduction

The gut microbiota is the immense collection of bacteria, viruses, eukaryotes and archea within our gastrointestinal (GI) tract (Butler et al., 2019; Lurie-Weinberger & Gophna, 2015; Matijašić et al., 2020). It has been described as a “forgotten organ”, with numerous critical functions as well as implications in health and disease (Clarke et al., 2014; Sun et al., 2022). Breaking down complex carbohydrates whilst producing short chain fatty acids (O’Riordan et al., 2022) and providing defence against pathogenic species and infections (Wiertsema et al., 2021), are just a few of the essential benefits of these organisms. Additionally, the gut microbiota aids in the production of a number of neurochemicals and other neuroactives, and modifies the availability of their precursors, resulting in important gut-brain axis signalling and CNS function (Valles-Colomer et al., 2019). There is evidence to suggest that it may play a role in mood regulation both in animal and human clinical studies (Bastiaanssen et al., 2019; Kelly et al., 2016). More recently, it was found that there were strong associations between the gut microbiota and depressive symptoms (Bosch et al., 2022) and more specificially, several microbial taxa were associated with depressive symptoms in individuals (Radjabzadeh et al., 2022). Alterations in microbial taxa within the gut have also been associated with increased externalizing behavior at the age of 10, a risk factor for the developing mental health disorders and risky lifestyles later in adulthood (Ou et al., 2022).

The gut microbiota, along with the GI tract and CNS, as well as communication conduits such as the autonomic nervous system, the enteric nervous system, the hypothalamic-pituitary-adrenal (HPA) axis and the immune system comprise the microbiota-gut-brain axis (Cryan et al., 2019). Being very complex, communication along the microbiota-gut-brain axis is bidirectional (Järbrink-Sehgal & Andreasson, 2020). Gut micro-organisms and their metabolites may stimulate vagal afferent fibres, resulting in direct transmission of signals to the CNS (Berding et al., 2021). Other microbial products may reach the brain in an “endocrine” fashion via the blood circulation and crossing the blood brain barrier. Likwise the opposite is possible, whereby the brain may influence the GI tract and it’s associated microbiota via the circulation and also by way of neuronal pathways of the vagus nerve. Disturbances in brain-gut-microbiota communication may influence eating behaviors, resulting in consumption of foods which have the potential to alter the gut microbiome (Gupta et al., 2020).

Dietary fiber, defined as edible, non-digestible (resistant to endogenous digestive enzymes), mostly plant-derived carbohydrate polymers consisting of at least 3 monomeric units (ISAPP, 2018; Makki et al., 2018), has numerous benefits to health. These include cardiovascular, gastrointestinal and mental health benefits just to mention a few (Barber et al., 2020). It is thought that many of these beneficial functions are exhibited via the gut microbiome (Lancaster et al., 2022). Dietary fiber has the potential to alter gut microbiota composition and function and promotes the production of metabolites such as SCFAs (Barber et al., 2020; Myhrstad et al., 2020). High fiber diets appear to facilitate microbiota enzymatic function leading to an immune response that is dependent on the individual (Wastyk et al., 2021).

Apart from SCFAs, bacterial enzymes produce numerous other biomolecules, many of which are relevant to brain function and behavior. This is encompassed within the concept of gut brain modules (GBMs). GBMs are specific pathways, within each of which there is either production or degradation of a particular neurochemical requiring the action of specific enzymes (Spichak et al., 2021; Valles-Colomer et al., 2019). These pathways are thought to be integral to microbiota-gut-brain axis communication and involve specific microbes. In order to fulfill the criteria for a GBM, the micro-organism must possess each enzyme within the specified pathway (Spichak et al., 2021; Valles-Colomer et al., 2019). The gut microbiota, in this regard may therefore be involved in the regulation of certain neurotransmistters or their precursors and other molecules that can potentially have an effect on the CNS. GBMs were manually curated, following an extensive literature review, into a database by Valles-Colomer and colleagues. (Valles-Colomer et al., 2019 into a framework (Valles-Colomer et al., 2019) Using and this database, the analysis ofprevalence in gut microbiomes from specific contexts (e.g. in disease states or pre and post an intervention) can be used to evaluate the potential of the gut microbiome to produce chemicals with neuroactive potential.

Sequencing technologies for DNA/RNA have aided in the identification of particular taxa or species within fecal samples and allow for not only compositional analysis, but also functional analysis of the microbiome (Bastiaanssen et al., 2019). Two popular techniques are 16S rRNA gene/transcript sequencing and whole genome “shotgun” sequencing (WGS) (Bastiaanssen et al., 2019; Ranjan et al., 2016). Many published studies investigating compositional and functional changes in the gut microbiota e.g., after an intervention, have yielded publicly available microbiome datasets thanks to high-throughput sequencing technologies (Liu et al., 2021). Once retrieved, these datasets can then be processed through software programs and their sequences mapped to reference databases, with their taxonomy changes and/or function inferred (Bastiaanssen et al., 2022).

We intend to perform a systematic review to identify compositional gut microbiota alterations after interventions with forms of dietary fiber in healthy individuals. We aim to not only provide a narrative comparison of the compositional changes between studies, but also to select studies with publicly available datasets to reanalyse and identify predicted fiber-induced alterations in GBMs. This will allow us to provide a functional account of the gut microbial compositional alterations following dietary fiber interventions.

Methods

The systematic review will be conducted in accordance with the guidelines for the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) (Moher et al., 2015). Following the PICO framework, the population includes healthy individuals over 18 years of age, whereas the intervention would be dietary fiber. In the context of our study, healthy individuals arewould be defined as those without any significant acute or chronic illness. as indicated by, for example, a psychiatric or medical diagnosis.., or significant medical or psychiatric risk factors and excludes states of obesity and post-menopause. Depending on the study, the control would either be the within-study control group or participant pre-intervention. The outcome would be compositional and functional microbiota alterations. We will report the study population descriptions used in each study.

Ethics

As this study entails secondary research, and will not involve any animals or human subjects, ethical approval is not required. The gathering, accessing, processing or use of personal information that could potentially identify individuals will not occur. Some of the results from this systematic review will be used in further statistical analysis as explained previously.

Discussion

Different studies have suggested that various types of dietary fiber have an impact on the gut microbiota in terms of promoting compositional changes (Guan et al., 2021; Holscher, 2017). Some types of dietary fiber may promote certain taxa (Holscher et al., 2015). Other types of fiber may promote some taxa whilst reducing others (Kaczmarek et al., 2019; Ranaivo et al., 2022). An assortment of studies has examined the effect of dietary fiber on the gut microbiota in pathological states e.g. obesity (Mocanu et al., 2021) and ulcerative colitis (Fritsch et al., 2021). However, our interest lies in examining studies reporting the effects of dietary fiber interventions on the gut microbiota of healthy individuals.

By refining our search to only involve studies with healthy populations, we aim to achieve a greater appreciation for the baseline changes seen with dietary fiber interventions. Such restriction will help control or account for confounding factors that are evident in disease states (Jager et al., 2008). This approach will help us to not only update and confirm the compositional alterations, but also uncover the possibility of other genera of micro-organisms that may not have been accounted for during initial analysis. This is plausible, given the differences with software technologies, techniques and human expertise (Nearing et al., 2022; Tsou et al., 2020) as well as database updates since the studies were published.

The concept of reanalyzing datasets is not new. It has been used to uncover aspects of dysregulated immune responses to COVID-19 infection, as part of the infectious disease mechanism (Alberts et al., 2022). The technique has proved useful in terms of understanding the host response as well as identifying further candidates with diagnostic potential in other infections such as leprosy (Leal-Calvo & Moraes, 2020). It is thought that reanalysis of datasets is a potentially useful tool in the research of rare diseases and allows for the discovery of genetic disease variants (Setty et al., 2022). Importantly, reanalysis of datasets has been applied to microbiota-gut-brain axis research in which evidence of disease-related microbial metabolic pathway alterations were uncovered in illnesses such as Alzheimer’s Disease, schizophrenia, anxiety and depression (Spichak et al., 2021).

However, our intended use of dataset reanalysis, especially in terms of predicting GBM function in relation to dietary fiber interventions, to our knowledge has not been done before. There will be limitations to our study as we expect significant heterogeneity between selected studies. We anticipate that there will be significant variance not only in study methodology, but also in terms of dietary intervention types, healthy population characteristics (e.g. geographical location, ethnicity, sex), study design and methodology as well as compositional outcomes.

Study status

At this moment, our preliminary searches have been completed and our selection of studies and formal screening are in progress. Data extraction, synthesis and tabulation are to follow.