Prebiotics for people with cystic fibrosis

Objectives

This is a protocol for a Cochrane Review (intervention). The objectives are as follows:

To evaluate the benefits and harms of prebiotics for improving health outcomes in children and adults with CF.

Background

Description of the condition

Cystic fibrosis (CF) is a life‐limiting genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. It affects approximately 100,000 children and adults worldwide (Bell 2020). CF is a multisystem disease resulting in thick secretions predominantly affecting the lungs, gastrointestinal (GI) tract, pancreas and liver. Around 84% of people with CF have exocrine pancreatic insufficiency, requiring treatment with pancreatic enzyme replacement therapy (PERT) (Cystic Fibrosis Foundation Patient Registry 2020). In spite of treatment with PERT, many people have frequent GI symptoms (Smith 2020). Children with CF may struggle to gain weight adequately and adults may have difficulty maintaining a healthy weight (Stallings 2008). The growth and nutritional status of people with CF is important as they are major determinants of lung function and survival (Corey 1988; Jadin 2011). Around half of people with CF achieve an adequate nutritional status (McCormick 2010; Turck 2016); and many children with CF fail to achieve catch‐up weight gain. However, a new era of CF care has increased lifespan and decreased symptoms in many people with CF, necessitating a re‐examination of the legacy diets in CF (McDonald 2021).

The GI microbiota play a significant role in health and disease, contributing to immunity, inflammation and metabolic function (Sekirov 2010). People with CF exhibit a disordered gut microbial ecosystem (Burke 2017; de Freitas 2018; Madan 2012; Nielsen 2016; Vernocchi 2018). Evidence suggests that, in CF, gut microbial dysbiosis occurs within the first year of life and the microbial imbalance develops further with increasing age when compared to healthy people (Nielsen 2016). Alterations of the gut microbiota in CF is likely to be multifaceted, but it is hypothesised to be due to the dehydrated, acidic luminal environment and thick mucous within the gut (Lee 2012), which adheres to the gut wall (Kelly 2022; Snyder 1964). Coupled with frequent antibiotic therapy (Duytschaever 2011), and high‐caloric, high‐fat diets (Sutherland 2018; Tomas 2016), this leads to a dysregulated gut microbiota in CF. The intestinal dysbiosis in children with CF is associated with impaired innate immunity (Ooi 2015). Longitudinal studies on the developing respiratory and intestinal microbiomes in infants and children with CF show that colonisation of pathogenic bacteria precedes in the gut followed by the lungs, increased microbial diversity was associated with better health (Hoen 2015; Madan 2012); and beneficial bacteria were reduced in CF over the first year of life (Antosca 2019).

Modern advances in high‐throughput screening have facilitated the fast‐tracking of CFTR modulators as a treatment. These modulator drugs have the ability to enhance or even restore the functional expression of specific CF‐causing mutations. People with CF treated with the CFTR modulator ivacaftor for 48 weeks showed a 2.7 kg increase in weight compared to those treated with placebo (Ramsey 2011); and body mass index (BMI) significantly improved after 24 weeks of the CFTR modulator combination treatment ivacaftor–tezacaftor–elexacaftor (marketed in the UK and EU as Kaftrio) with a mean treatment difference (z score) of 1.04 relative to placebo (Middleton 2019). Furthermore, emerging evidence suggests that CFTR‐modulating therapy may improve the irregular pathophysiology of the gut in people with CF and cause a favourable change in the resident gut microbiota (Ooi 2018). Furthermore, preliminary evidence suggests the use of the CFTR modulator ivacaftor–tezacaftor–elexacaftor may lead to an improvement in gut symptoms (Mainz 2022).

Description of the intervention

Dietary interventions to target the gut microbiota may provide suitable adjunct treatment alongside CFTR modulators in people with CF. Dietary strategies to target the gut microbiota include probiotics, defined as live micro‐organisms that, when administered in adequate amounts, confer a health benefit on the host (Hill 2014). Alternatively the concept of a 'prebiotic' was first proposed in 1995 by Gibson and Roberfroid (Gibson 1995). In the decades that followed, prebiotic research focused on substrates that targeted health‐promoting groups of bacteria in the gut (commonly those of the genera bifidobacteria and lactobacilli). Prebiotics have been studied for certain health effects, such as immune modulation, cardiometabolic health, infection reduction and mineral availability (Sanders 2019; Scott 2020).

The most up‐to‐date scientific definition of a prebiotic was developed through the International Scientific Association for Probiotics and Prebiotics in 2016 (Gibson 2017). This current consensus definition is "a substrate that is selectively utilised by host microorganisms conferring a health benefit". Therefore, the prebiotic concept includes three essential parts: a substance, a physiologically beneficial effect and a microbiota‐mediated mechanism. Prebiotics are frequently equated with dietary fibres, but only a subset of fibres qualify as prebiotics. Prebiotics may be present naturally in whole plant foods (e.g. chicory root, onions and bananas) or in synthesised forms. Most research into their health effects has focused on isolated substances (Savino 2022). The most studied are the soluble fibre‐prebiotic inulin, fructo‐oligosaccharides (FOS), and galacto‐oligosaccharides (GOS) and, more recently, human milk oligosaccharides (HMOs). Candidate oligosaccharides with less evidence to date include malto‐oligosaccharide, isomalto‐oligosaccharide and xylo‐oligosaccharide; and candidate non‐fibre prebiotics include conjugated linoleic acid, polyunsaturated fatty acids and polyphenols. At present, there are no official dietary recommendations on intake or daily allowances for prebiotics in healthy individuals.

Most dietary 'biotic' research has focused on the use of probiotics in health conditions with more limited evidence for the use of prebiotics. As of 2020, 11 completed randomised controlled trials (RCTs) on probiotics in people with CF had been conducted (Coffey 2020). That Cochrane Review highlighted that due to the variability of probiotic composition and dosage, further adequately powered multicentre RCTs of at least 12 months' duration are required to best assess the efficacy and safety of probiotics for children and adults with CF (Coffey 2020). With evidence highlighting that prebiotic approaches can target multiple genera of beneficial bacteria (Gibson 2017), they may provide a viable alternative or synbiotic alongside dietary probiotics.

How the intervention might work

The presence of dysregulated intestinal microbial ecosystem and inflammation is well established in CF (de Freitas 2018; Dhaliwal 2015; Nielsen 2016). Prebiotics are hypothesised to alter the microbial growth and activity of beneficial resident microbes in the gut and therefore may restore the gut microbial profile towards 'normal'. There is some emerging evidence to support a potential role for prebiotics in reducing the risk and severity of GI infection and inflammation (Gibson 2005; Licht 2012), including diarrhoea and inflammatory bowel disease (Silk 2009). Prebiotics may also play a role in bone and mineral absorption (Whisner 2018), and data suggest that they reduce the risk of obesity by promoting satiety and weight loss (Kellow 2014); and may improve glycaemic control and gut permeability in type 1 diabetes (Ho 2019).

The mechanistic effect of prebiotics to act upon health is multifaceted. Primarily they are selectively utilised by beneficial bacteria within the gut (commonly from the genuses Lactobacilli and Bifidobacterium), increasing the growth and activity of the host's beneficial bacteria. Beneficial bacteria within the gut can then modulate immune function, improve gut barrier integrity, produce antimicrobial compounds and promote enzyme formation (Sanders 2019). Furthermore, the selective fermentation of prebiotic compounds by beneficial bacteria in the gut results in the production of short‐chain fatty acids (SCFAs), which are known to have positive immunomodulatory effects (Corrêa‐Oliveira 2016). Prebiotic compounds can also act directly on the intestinal epithelial cells to influence inflammatory signalling (Newburg 2016); and have direct immunomodulatory effects on macrophages (Searle 2012). A diverse and active gut microbial ecosystem will have an impact on intestinal health and homeostasis and confers direct, indirect and systemic benefits on:

1. lung health (gut–lung axis potentially through immune‐mediated cross‐talk) (Hoen 2015; Marsland 2015);

2. brain health and mental wellbeing (gut–brain axis) (Burokas 2017; Dinan 2017; Marques 2014); and

3. general health outcomes such as growth (Dhaliwal 2015).

Why it is important to do this review

Alterations to the host microbiome and intestinal inflammation is a largely understudied in people with CF and could be a potential therapeutic target to improve disease management. One 2015 survey found that 60% of children in a CF clinic in the USA were self‐medicating with probiotics (Sullivan 2015); and recently one Australian study showed similar with 70% of adults with CF having used probiotics, attributed to GI and antibiotic issues (Anderson 2022). The authors of one recent Cochrane Review on probiotics for people with CF concluded "To the best of our knowledge, many clinics and people with CF regularly use probiotics, despite the limited evidence. Overall, this approach is likely safe and may have some limited health benefits" (Coffey 2020).

These studies highlight that probiotic use in CF is prevalent but also highlight there is no consensus and direct recommendations on the use of probiotics in CF. GI symptoms are important considerations for adults with CF, further antibiotic use is prevalent and subsequently dietary prebiotics could be suitable adjunct treatments in the disease. Indeed, prebiotic supplementation have been shown to reduce GI symptoms in irritable bowel disease (Silk 2009), and reduce diarrhoea associated with travel (Drakoularakou 2010) and antibiotic use (Guridi 2020). Given the continued emerging evidence for the importance of diet and the gut microbiota in people with CF (Li 2014; Madan 2012), and positive alterations in gut microbiota and disease outcomes with CFTR modulation therapy (Ooi 2018), understanding the current evidence base for prebiotics in CF will be useful for the wider CF community. This could justify future research programmes and application for patient benefit.

Objectives

To evaluate the benefits and harms of prebiotics for improving health outcomes in children and adults with CF.

Methods

Criteria for considering studies for this review

Types of studies

We will include RCTs and quasi‐RCTs to assess the efficacies of prebiotics in children and adults with CF. We will only include the first treatment period from cross‐over RCTs regardless of washout period (Elbourne 2002). A washout phase is designed to limit potential residual treatment effects, but as there is no consensus on washout durations for prebiotic interventions, we will not define a minimum duration here.

Types of participants

We will include participants who fulfil consensus diagnostic criteria for CF (Farrell 2017). We will place no restrictions for participants in terms of age, gender, genotype, pancreatic exocrine sufficiency status, disease severity, comorbidities, antibiotic use or CFTR modulator therapy.

Types of interventions

We will compare any oral fibre‐prebiotic (inulin, FOS and GOS, dose or formulation, without a probiotic) to any other prebiotic formulation, probiotic or synbiotic, or placebo or no control treatment. We will include trials where participants could be randomised to a prebiotic‐only arm with a suitable comparator.

We will include trials using both single and combined fibre‐prebiotic interventions of inulin, FOS and GOS. We will exclude candidate fibre prebiotics of resistant starch, polydextrose, xylo‐oligosaccharide, imalto‐oligosaccharide and isomalto‐oligosaccharide due to the lack of evidence to accepted them as qualified prebiotics. Similarly, we will exclude candidate non‐fibre prebiotics polyphenolics, and polyunsaturated fatty acids.

We will exclude in vitro trials or trials examining the effect of probiotics alone or synbiotics (without adequate description on dose of prebiotic and type of prebiotic used).

Types of outcome measures

We will assess the following outcome measures at three‐monthly intervals for the first 12 months and then annually thereafter. This will allow us to account for the likelihood of each study reporting outcomes at different durations of fibre‐prebiotic interventions. Pooling all outcomes reported from one to three months or from four to six months will allow for comparison between trials. The time points here relate to the duration of the prebiotic intervention.

Primary outcomes

1. Growth and nutrition (mean change from baseline and post‐treatment absolute mean)

   1. height (cm and z score)

   2. weight (kg and z score)

   3. BMI (kg/m² and z score)

2. GI symptoms measured using the multimodal questionnaire for the assessment of abdominal symptoms in people with cystic fibrosis (CFAbd Score) (Jaudszus 2019)

3. Adverse events in response to prebiotic feeding, number of participants experiencing an adverse event categorised according to severity

   1. mild transient event (e.g. nausea, diarrhoea)

   2. moderate event (treatment discontinued, e.g. nephrotoxicity)

   3. severe event (e.g. hospitalisations)

   4. adverse events leading to withdrawal

Secondary outcomes

1. Pulmonary exacerbation defined using consensus criteria, Fuchs' criteria (Fuchs 1994)

   1. number of pulmonary exacerbations

   2. duration of antibiotic therapy (any route) for pulmonary exacerbations (days), separated by route of delivery for intravenous or oral delivery

2. Lung function (mean change from baseline and post‐treatment absolute mean)

   1. forced expiratory volume in one second (FEV₁) % predicted

   2. forced expiratory volume in one second (L)

   3. lung clearance index (LCI)

3. Inflammatory biomarkers (mean change from baseline and post‐treatment absolute mean)

   1. intestinal

      1. calprotectin (μg/g)

      2. M2‐PK (U/mL)

      3. rNO (μmol/L)

   2. serum

      1. C‐reactive protein (CRP) (mg/L)

      2. cytokines (pg/mL)

4. Hospitalisations (all causes)

   1. number

   2. duration (days)

5. Health‐related quality of life (HRQoL) measured using a validated questionnaire (e.g. Cystic Fibrosis Questionnaire – Revised (CFQ‐R); Quittner 2009)

6. Intestinal microbial profile assessed using next‐generation sequencing of stool samples as a change from baseline in response to intervention.

   1. alpha diversity (e.g. richness or Shannon index)

   2. beta diversity (e.g. Bray‐Curtis dissimilarity)

7. Faecal short chain fatty acids (mmol/kg) – total and individual (acetate, propionate, butyrate) with reference to wet and dry weight

8. Change in body composition (e.g. via iDexa scan, including fat mass and fat‐free mass, and mid‐arm girth)

Search methods for identification of studies

We will search for all relevant published and unpublished trials without restrictions on language, year or publication status.

Electronic searches

The Cochrane Cystic Fibrosis and Genetic Disorders Group's Information Specialist will conduct a search of the Group's Cystic Fibrosis Trials Register for relevant trials using the following terms: prebiotics.

The Cystic Fibrosis Trials Register is compiled from electronic searches of the Cochrane Central Register of Controlled Trials (CENTRAL) (updated each new issue of the Cochrane Library), weekly searches of MEDLINE, a search of Embase to 1995 and the prospective handsearching of two journals ‐ Pediatric Pulmonology and the Journal of Cystic Fibrosis. Unpublished work is identified by searching the abstract books of three major cystic fibrosis conferences: the International Cystic Fibrosis Conference, the European Cystic Fibrosis Conference and the North American Cystic Fibrosis Conference. For full details of all searching activities for the register, please see the relevant section of the Cochrane Cystic Fibrosis and Genetic Disorders Group's website (cfgd.cochrane.org/our-specialised-trials-registers).

We will search the following databases and registries; the search strategies are presented in the appendices (Appendix 1):

1. PubMed (from 1946 to present);

2. US National Institutes of Health Ongoing Trials Register ClinicalTrials.gov (www.clinicaltrials.gov);

3. World Health Organization International Clinical Trials Registry Platform (ICTRP) (trialsearch.who.int/).

Searching other resources

We will check the bibliographies of included trials and any relevant systematic reviews identified for further references to relevant trials. We will also contact experts and organisations in the field to obtain additional information on relevant trials.

Data collection and analysis

We will employ the standard methods of the Cochrane Cystic Fibrosis and Genetic Disorders Group and the Cochrane Handbook of Systemic Reviews of Interventions (Higgins 2022a).

If an author is involved in any trial identified, they will not participate in decision‐making regarding inclusion or exclusion from the review. Furthermore, they will not make any risk of bias assessments or extract data for that trial.

Selection of studies

Once we have identified a complete list of references, one review author (NW) will check and remove duplicates and will then enter the list into the Covidence online software (Covidence). Two review authors (NW and JJ) will independently assess abstracts and titles and exclude obviously relevant trials. We will assess the full text of each remaining trial to determine which satisfy the inclusion criteria. We will discuss any discrepancies, and gain a consensus through the use of a third review author (AP). We will report reasons for exclusion of trialsat the full‐text stage in the 'Characteristics of excluded studies' table.

Data extraction and management

Two review authors (NW and JJ) will independently extract data using a standard data extraction form in Covidence (Covidence), and we will pilot this on three trials. We will collect data to complete a 'Characteristics of included studies' table on:

1. participant characteristics (e.g. age, gender, genotype, phenotype, pancreatic status);

2. trial characteristics and design (e.g. RCTs or quasi‐RCT);

3. interventions and comparator (e.g. type of fibre‐prebiotic, dose, duration);

4. outcome data – reported separately for each outcome.

We will list all treatment arms in the 'Characteristics of included studies' table, even if they are not used in the review.

We will discuss any discrepancies and gain a consensus through the use of a third review author (AP). Where data may be incomplete, we will contact the primary investigator to request further information and clarification. If we identify multiple publications from one trial, we will group reports. 
We will import the extracted data into Review Manager Web for analysis (RevMan Web 2022).

We will report the contribution of a prebiotic (independent of any co‐administered agents) to any change in gut symptoms. We will compare any oral fibre‐prebiotic (dose or formulation without a probiotic) to any other prebiotic formulation, probiotic or synbiotic, or placebo, or no control treatment. This will be followed by subgroup analysis on individual types of prebiotic for outcomes with sufficient data.

We anticipate that studies are likely to report at different time points. Therefore, we plan to group outcome data based on the duration of the fibre‐prebiotic intervention into three‐monthly intervals for the first 12 months and then annually thereafter to allow for comparisons between studies. The impact of intervention duration will be explored further in a subgroup analysis.

Where studies are not reported in English, we will attempt to translate to allow for appropriate data extraction.

Assessment of risk of bias in included studies

To assess the risk of bias we will use the risk of bias tool described in the Cochrane Handbook of Systematic Reviews for Interventions (Higgins 2017). Two review authors (NW and JJ) will independently assess the risk of bias for each included trial across the following six domains:

1. sequence generation;

2. allocation concealment;

3. blinding (self‐reported and objective);

4. incomplete outcome data;

5. selective reporting; and

6. other potential sources of bias.

We will discuss any discrepancies and gain a consensus with a third review author (AP). We will judge a trial to have a low risk of bias for randomisation if it describes the randomisation and allocation processes, including concealment from the researchers. If these processes are inadequate, we will deem the trial at high risk of bias or if unclear we will deem the trial at unclear risk of bias. To assess blinding, we will determine who was blinded, and the method used to determine the risk of bias. Two review authors (NW and JJ) will examine missing data, the distribution of the missing data, and how investigators managed withdrawals and loss to follow‐up. If a trial includes an intention‐to‐treat (ITT) analysis, we will deem this to minimise the risk of bias.

We will assess outcome reporting by reviewing the outcomes to be measured, either in the trial paper or a published protocol. If trial investigators measured relevant outcomes but do not report these, then we will deem that as a high risk of bias. We will summarise data from individual trials in a risk of bias table. We will not exclude trials on the basis of risk of bias.

Assessment of bias in conducting the systematic review

We will conduct the review according to this published protocol and report any deviations from it in the 'Differences between protocol and review' section of the systematic review.

Measures of treatment effect

For continuous outcomes, we will record the mean change and standard deviation (SD) from baseline for each group (prebiotic and placebo) or mean post‐treatment or intervention values and SD or standard error (SE) for each group. We will calculate a pooled estimate of treatment effect for each outcome using mean difference (MD) with 95% confidence intervals (CIs) or standardised mean difference (SMD) with 95% CIs depending on the variability of the outcome measures.

For dichotomous outcomes (e.g. adverse events), we will record the number of participants with an event and the number of participants analysed in each group. Where appropriate, we will present a pooled estimate of the treatment effect for each outcome across trials using risk ratio (RR) with 95% CIs.

Unit of analysis issues

Trials with a parallel group design will include the individual participant as the unit of analysis.

We will only include the first treatment period from cross‐over RCTs regardless of washout period (Elbourne 2002). A washout phase is designed to limit potential residual treatment effects, but as there is no consensus on washout durations for prebiotic interventions, we will not define a minimum duration here.

In trials with more than one intervention group, first we will determine if all interventions are relevant to this systematic review and meta‐analysis; and then determine how we will include a trial with more than one relevant intervention for a particular meta‐analysis. For multi‐arm trials, the intervention groups of relevance will be those that we can include in a pair‐wise comparison of intervention groups that meet the predetermined criteria for inclusion in the review. We will not include comparisons to non‐relevant intervention groups. To avoid any confusion over the identity and nature of each trial, we will report all intervention groups of a multi‐intervention trial in the 'Characteristics of included studies' table. However, we will provide only detailed descriptions the intervention groups relevant to the review, and only use these groups in analyses. For multiple group studies, where all groups have received a relevant intervention compared to a control, we will combine the intervention groups to create a single pairwise comparison to the control group (Higgins 2022b).

Dealing with missing data

Initially, if extracted data are insufficient or unclear for the purposes of the review, we will contact the trial investigator(s). We will assess whether they have performed an ITT analysis for missing data and report the number of participants missing from each trial arm where possible. If only means and P values are reported from a trial, we will estimate the SDs using the Review Manager Web calculator (RevMan Web 2022).

Assessment of heterogeneity

We will assess heterogeneity between trials using the Chi² and I² statistics, and by visual inspection of overlapping CIs on forest plots (Higgins 2003). We will consider a P value of less than 0.1 of interest for the Chi² test. Following the Cochrane Handbook for Systematic Reviews of Interventions, we will interpret the I² statistic as (Deeks 2022):

1. 0% to 40%: might not be important;

2. 30% to 60%: may represent moderate heterogeneity;

3. 50% to 90%: may represent substantial heterogeneity;

4. 75% to 100%: considerable heterogeneity.

Assessment of reporting biases

We will attempt to minimise the likelihood of reporting bias from the non‐publication of trials or selective outcome reporting by using a broad search strategy, including trial registries. If a sufficient number of trials have reported a given outcome (at least 10 trials) (Sterne 2011), we will use funnel plots to assess for bias.

Data synthesis

Where possible we will combine trials in a meta‐analysis using Review Manager Web (RevMan Web 2022). If different trials use multiple types of prebiotics, then we will use a random‐effects model.

Subgroup analysis and investigation of heterogeneity

In the presence of heterogeneity (I² statistic of 50% and greater) we will conduct the following subgroup analyses:

1. infants and preschool (aged one to five years) versus older children (aged six to 18 years) versus adults (aged over 18 years);

2. concurrent modulator therapy versus no modulator therapy;

3. fibre‐prebiotic type (inulin versus FOS versus GOS); and

4. fibre‐prebiotic duration (one to less than six months versus six to 12 months).

Sensitivity analysis

If we identify sufficient studies to combine in a meta‐analysis, we will undertake a sensitivity analysis including or excluding trials that we judge to have either a high or unclear risk of selection bias (judgement made as detailed in Assessment of risk of bias in included studies). We will conduct a further sensitivity analysis including or excluding trials with a high risk for the domains of performance bias and detection bias.

Summary of findings and assessment of the certainty of the evidence

We propose to generate a summary of findings table for each comparison we present and will report each of the following outcomes:

1. change in weight (kg) (follow‐up to six months);

2. change in BMI (follow‐up to six months);

3. GI symptoms (from the validated CFAbd‐Score) (follow‐up to six months);

4. adverse events (at four to six months);

5. number of pulmonary exacerbations (at four weeks to 12 months);

6. change in FEV₁ % predicted (at four weeks to 12 months);

7. change in intestinal calprotectin (μg/g) (at four weeks to 12 months); and

8. changes in HRQoL score(s) (at four weeks to 12 months).

The choice of these outcomes is based on relevance to clinicians and patients. Two review authors (NW and JJ) will independently use the GRADE approach to determine the overall certainty of the evidence for each outcome (Schünemann 2022). For each outcome, we will report the population, setting, intervention, comparison, illustrative comparative risks, magnitude of effect (RR or MD or SMD), number of participants and trials, a GRADE score and additional comments. We will justify downgrading or upgrading the certainty of the evidence in footnotes.

Appendices

Appendix 1. Electronic search strategies

Database	Search strategy
PubMed (from 1946 to present)	#1 Cystic fibrosis OR mucoviscidosis #2 prebiotic OR synbiotic OR galactooligosaccharide OR fructooligosaccharide OR Inulin OR oligosaccharide #3 randomized controlled trial [pt] #4 controlled clinical trial [pt] #5 randomized [tiab] #6 placebo [tiab] #7 drug therapy [sh] #8 randomly [tiab] #9 trial [tiab] #10 groups [tiab] #11 #3 OR #4 OR #5 OR #6 OR#7 OR #8 OR #9 OR #10 #12 animals [mh] NOT humans [mh] #13 #11 NOT #12 #14 #1 AND #2 AND #13 Note: Lines #3 ‐#13 are the Cochrane Highly Sensitive Search Strategy for identifying randomized trials in MEDLINE: sensitivity‐maximizing version (2008 revision); PubMed format. Available from: https://training.cochrane.org/handbook/version‐6/chapter‐4‐tech‐suppl, page 60
ClinicalTrials.gov (www.clinicaltrials.gov)	[Advanced Search] Condition or disease: Cystic fibrosis OR mucoviscidosis Other terms: prebiotic OR synbiotic OR galactooligosaccharide OR fructooligosaccharide OR Inulin OR oligosaccharide Study type: Interventional Studies (Clinical Trials)
WHO ICTRP (trialsearch.who.int/)	[Basic Search] (Cystic fibrosis OR mucoviscidosis) AND (prebiotic OR synbiotic OR galactooligosaccharide OR fructooligosaccharide OR Inulin OR oligosaccharide)

Contributions of authors

Task	Author(s) responsible
Protocol stage: draft the protocol	NW, JJ
Review stage: select which trials to include (2 + 1 arbiter)	NW, JJ, AP
Review stage: extract data from trials (2 people)	NW, JJ
Review stage: enter data into Review Manager Web	NW, JJ
Review stage: carry out the analysis	NW, JJ
Review stage: interpret the analysis	NW, JJ, SN, AP, AS
Review stage: draft the final review	NW, JJ, AP, SN, AS
Review stage: draft the final review	NW, JJ, AP, SN, AS
Update stage: update the review	NW, JJ

Sources of support

Internal sources

• No sources of support provided

External sources

• National Institute for Health Research, UK

  This systematic review was supported by the National Institute for Health Research, via Cochrane Infrastructure funding to the Cochrane Cystic Fibrosis and Genetic Disorders Group.

Declarations of interest

NW has received prebiotic supplement in‐kind support from Clasado Biosciences. Providing prebiotic product for previous research trial (Williams 2016).

JJ: none.

AP Conflicts of interest TBC.

SN: none.

AS reports grants from Vertex, as well as speaker honoraria and expenses from Vertex and Teva, outside the submitted work. In addition, ARS has a patent issued "Alkyl quinolones as biomarkers of Pseudomonas aeruginosa infection and uses thereof".

New