Synbiotics, prebiotics and probiotics for solid organ transplant recipients

Objectives

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

This review aims to look at the benefits and harms of synbiotics, prebiotics, and probiotics for people undergoing, or recipients of, solid organ transplantation.

Background

Description of the condition

Solid organ transplantation

Solid organ transplant involves the transplant of a heart, kidney, liver, lung, or pancreas from a living or deceased donor. It is often the only treatment for end‐stage organ failure, particularly for liver and heart failure (WHO 2020). For kidney failure, whilst kidney replacement therapies are common, kidney transplantation is generally accepted as the best treatment both for quality of life and cost effectiveness (WHO 2020).

Solid organ transplantation is one of the largest therapeutic medical advancements of the 20th century (Linden 2009). Starting from what were clinical experiments, improvements in both surgical techniques and immunosuppression achieved prolonged recipient survivals, resulting in transplantation proven to be clinically effective, life‐saving, and cost‐effective (Linden 2009). Organ transplant is not without its risks and ethical concerns such as organ trafficking and transplant tourism (Declaration of Istanbul 2008).

The Global Observatory on Donation and Transplantation reported 146,840 organs transplanted annually in 2018, a 5.6% increase over 2017 (GODT 2020). By organ type, 95,479 kidney (64% deceased donors), 34,074 liver (80.5% deceased donors, 0.1% domino), 8,311 heart, 6,475 lung, 2,338 pancreatic, and 163 small bowel transplantations (GODT 2020).

Immunosuppression

To reduce the chance of graft rejection and other complications, immunosuppressive medications are standard post‐transplant treatment. Essential to graft acceptance and effective recovery, doses will vary depending on the age, organ type, and health status of the individual patient. Immunosuppressive mediations are often accompanied by high prevalence of gastrointestinal symptoms and gut intolerance side effects (Lehto 2018; Luyckx 2018). Apart from gastrointestinal side effects, long‐term exposure to immunosuppressive medications has seen an increase in drug‐related morbidities such as: cardiovascular disease, diabetes mellitus, hyperlipidaemia, hypertension, and malignancy (Sudan 2007). In the paediatric population, children differ in their immune responses, in the way they metabolize drugs, and in their susceptibility to adverse effects of transplantation and immunosuppression (Sudan 2007). Non‐compliance to immunosuppressive treatments, particularly among adolescents, can lead to increased risk of graft failure (Sudan 2007).

The gut microbiome

The human microbiome is the collective genomes of the micro‐organisms in a particular environment (Valdes 2018) and is of emerging high interest in chronic disease research. The human gut microbiota includes fungi, bacteria, archaea, protozoa, and viruses that all interact with each other, and the host, to affect the host's health and physiology (Azad 2018). The human intestine hosts more than 10 billion micro‐organisms of which the microbial composition changes person to person, along both the digestive tract and within the urinary and kidney environments (Aron‐Wisnewsky 2016). Recent culture‐independent studies that use high‐throughput sequencing have indicated that any microbial imbalances (otherwise known as gut dysbiosis or leaky gut) may be associated with cardiometabolic diseases in the long term (such as allergy, asthma, inflammatory bowel disease, celiac disease, systemic lupus erythematous, arthritis, chronic kidney disease (CKD), diabetes obesity, and cardiovascular disease (CVD)) (Aron‐Wisnewsky 2016; Bromberg 2015). In people with advanced stages of CKD, uraemia alters the biochemical milieu, promoting disturbances in gut microbiota (the community or micro‐organisms themselves (Valdes 2018)) and the intestinal barrier (Mafra 2019). Furthermore, it is reported around 30% of transplant recipients experience some form of gastrointestinal side effects during treatment and follow‐up.

Current evidence suggests a link between gut microbiome and CKD in particular the production of putative uraemic toxins (e.g. indoxyl sulfate, p‐cresol sulfate, phenylacetylglutamine, trimethylamine‐N‐oxide, kynurenine), increased gut permeability, and transmural movement of bacteria and endotoxins and inflammation (Beerepoot 2016; Cremon 2018; Lehto 2018; Luyckx 2018).

Description of the intervention

Early observational and intervention studies have been investigating food‐intake patterns, various synbiotic interventions (antibiotics, prebiotics, or probiotics), and faecal transplants to measure their effects on microbiota in treating cardiometabolic diseases, in particular CKD (Aron‐Wisnewsky 2016).

Prebiotics

The International Scientific Association for Probiotics and Prebiotics (Gibson 2017) defines prebiotics as substrates, or non‐digestible dietary substances, that are selectively utilised and fermented within the small intestine by host micro‐organisms. By modifying or diversifying the host microbiota this may induce a health benefit to the host. Most types of prebiotics are subsets of carbohydrate groups and mostly oligosaccharide carbohydrates (Davani‐Davari 2019).

• Fructans: inulin and fructo‐oligosaccharides (stimulate the enrichment of native probiotics Lactobacilli and Bifidobacteria)

• Galacto‐oligosaccharides (also known as trans‐galacto‐oligosaccharides): (stimulate the enrichment of native probiotics Lactobacilli, Bifidobacteria, Enterobacteria, Bacteroidetes, and Firmicutes)

• Starch and glucose‐derived oligosaccharides: resistant starch, polydextrose

• Other oligosaccharides: pectic‐oligosaccharide (from the poly saccharide pectin)

• Non‐carbohydrate oligosaccharides: cocoa‐derived flavanols.

Natural sources of prebiotics can be obtained in peas, beans, cow's milk, human breast milk, soybean, rye, tomato, barley, wheat, honey, banana, onion, chicory, garlic, sugar beet, asparagus, and artichoke.

Probiotics

The term probiotics are used to describe live micro‐organisms that are intended to confer health benefits on the host when administered in adequate quantities (FAO/WHO 2002). The living bacteria that may modulate the existing composition of gut microbiota in an attempt to improve the health of the gastrointestinal tract, the immune system, inflammatory state and the "bioavailability of micronutrients" (Cremon 2018). The key microbial organisms often found in probiotic treatments are:

• Lactobacillus

• Bifidobacterium

• Saccharomyces

• Streptococcus

• Enterococcus

• Escherichia

• Bacillus

Natural sources of probiotics can be obtained in fermented foods such as yoghurt, kimchi, kombucha, sauerkraut, miso, pickles, raw apple cider vinegar, kefir, tempeh, some cheeses, and some sourdough breads.

Synbiotics

Synbiotics are the combination of prebiotics and probiotics in the one treatment with the intention of producing a superior effect compared to either agent along (Pan 2018). The effect is currently unknown.

Synthetic versions of synbiotics, prebiotics, and probiotics are available as oral capsules, tablets, liquids, or powder forms over‐the‐counter in most developed countries (Cremon 2018).

How the intervention might work

It is believed through growing research that high doses of synbiotics, prebiotics, and probiotics are able to modify and improve dysbiosis of gut micro‐organisms by altering the population of the micro‐organisms. With the right balance in the gut flora, a primary benefit is (believed to be) the suppression of pathogens through immunostimulation, and gut barrier enhancement (less permeability of the gut) (Cremon 2018).

The gut microbiota ferments prebiotics and produces short‐chain fatty acids (lactic acid, butyric acid, propionic acid) which have positive effects on the airways, dendritic cells in bone marrows, and decreases the pH of the colon (Davani‐Davari 2019). Prebiotics also decrease the gut pH resulting in the butryogenic effect ‐ where a slight change in the unit of change in pH alters the entire composition or population of acid‐sensitive species (Bacterioids) and promotes butyrate formation of Firmicutes (Davani‐Davari 2019).

Probiotics alter the intestinal pH, inhibit pathogens (via the generation of antibacterial compounds, competitively eliminating pathogens in receptor binding sites and compete for available nutrients), inhibit mutagenic and carcinogenic production, and maintain the intestinal barrier (Kato 2008).

Why it is important to do this review

Prebiotics and probiotics are freely available as over‐the‐counter purchases in most developed countries and are being utilised as therapeutic supplements for improving the function and balance of gut microbiota in the general population. Whilst many positive effects have been identified, the exact mechanism of action by which these compounds exert their beneficial actions in human is only partially understood (Cremon 2018). In the general population, there is no definitive data to support the use of synbiotics, prebiotics, or probiotics. In solid organ transplant populations, there are uncertain effects because of potential immunosuppressive effects and the risk of catastrophic infections with the live micro‐organisms. The efficacy of these interventions, and certainty of the evidence, in these patients remains unknown, thus it is imperative to synthesise the benefits and harms associated with these treatments.

Objectives

This review aims to look at the benefits and harms of synbiotics, prebiotics, and probiotics for people undergoing, or recipients of, solid organ transplantation.

Methods

Criteria for considering studies for this review

Types of studies

All randomised controlled trials (RCTs) and quasi‐RCTs (RCTs in which allocation to treatment was obtained by alternation, use of alternate medical records, date of birth or other predictable methods) and cluster RCTs.

Cross‐over studies will be included and data from the first phase only will be used for analysis.

Full journal publication and peer review is required. Abstracts will be included.

Studies in any healthcare setting will be included.

Excluded study design: single arm studies, commentaries, editorials, and clinical observations.

Types of participants

Inclusion criteria

• Adults and children with a solid organ transplant (heart, kidney, liver, lung, pancreas, or intestinal/short bowel)

• Single or multiple transplants

• Transplant from a living or deceased donor.

• Studies of populations with altered gastrointestinal function and co‐morbidities (such as diabetic kidney disease) will be included and analysed as subgroups.

Exclusion criteria

• Adults and children who have signs of systemic illness (such as fever, loin pain, toxicity).

Types of interventions

• Any synbiotic

• Any prebiotic

• Any probiotic

• Combination therapies of biotics with other biotic, pharmacological, or non‐pharmacological treatments

• Any dose, duration, administration, or frequency

• Any formulation: tablet, capsule, or powder

Participants receiving concurrent pharmacological medications for co‐morbidities such as blood glucose medications, blood pressure medications, immunosuppressants will be included and analysed as subgroups.

Studies of high‐dose prebiotics for the purpose of purgation and studies of dietary changes will be excluded.

Comparison pairs for analysis

• A synbiotic, prebiotic, or probiotic treatment compared to placebo

• A synbiotic, prebiotic, or probiotic treatment compared to no treatment

• A synbiotic, prebiotic, or probiotic treatment compared to another synbiotic, prebiotic, or probiotic treatment (A versus B)

• A synbiotic, prebiotic, or probiotic treatment compared to a pharmacological comparator (antibiotics, immunosuppressants, other medicines)

• A synbiotic, prebiotic, or probiotic treatment compared to a non‐pharmacological comparator (dietary, educational, behavioural, vitamin or herbal supplements, Traditional Chinese Medicine)

• Combinations of synbiotic, prebiotic or probiotic treatment with another biotic, another pharmacological, or another non‐pharmacological treatment compared to any of the above comparators

For each of these comparisons, synbiotics, prebiotics, and probiotics will be analysed as separate comparisons.

Dose, frequency, and duration will be analysed as separate comparisons.

Formulation will be analysed as subgroups.

Types of outcome measures

This review will not exclude studies based on non‐reporting of outcomes of interest.

The outcomes selected include the relevant SONG core outcome sets as specified by the Standardised Outcomes in Nephrology initiative (SONG 2017).

Primary outcomes

• Gastrointestinal function: change in any gastrointestinal upset or intolerance; microbiota composition; faecal characteristics (such as the Bristol Stool Chart) (Lewis 1997); colonic transit time

• Graft health: organ rejection, organ acceptance, graft infection

• Quality of life issues: using any validated quality of life scale

• Adverse events and serious adverse events

• Death and cause‐specific death

Secondary outcomes

• Blood pressure (BP): systolic (SBP), diastolic (DBP)

• Organ function measures

  • Cardiac function: echocardiogram

  • Kidney function: creatinine clearance, serum creatinine, albuminuria, proteinuria, dialysis, estimated glomerular filtration rate (eGFR)

  • Liver function measures: alanine transaminase; aspartate aminotransferase; alkaline phosphatase; albumin; bilirubin

  • Pulmonary function: peak expiratory flow (PEF); arterial blood gas; forced vital capacity (FVC); forced expiratory volume in one second (FEV1); forced expiratory ratio (FEV); FEV1/FVC

  • Pancreas function measures

• Relapse

• Pain: using any validated pain scale

• Patient satisfaction and convenience of treatment

• Treatment adherence

• Other outcomes (e.g. cardiovascular (BP, cardiovascular events, myocardial infarction (MI)))

• Use of immunosuppressants

• CVD

  • CVD markers: BP; lipids; vascular access; left ventricular mass index; peripheral vascular disease; cerebrovascular disease; coronary artery disease

  • CVD events: stroke, MI, heart failure, transient ischaemic attack

• Cancer

• Infection

• Life participation: return to normal activities; days absent from work/school; mental health and functional status

Search methods for identification of studies

Electronic searches

We will search the Cochrane Kidney and Transplant Register of Studies through contact with the Information Specialist using search terms relevant to this review. The Register contains studies identified from the following sources:

1. Monthly searches of the Cochrane Central Register of Controlled Trials (CENTRAL)

2. Weekly searches of MEDLINE OVID SP

3. Searches of kidney and transplant journals, and the proceedings and abstracts from major kidney and transplant conferences

4. Searching of the current year of EMBASE OVID SP

5. Weekly current awareness alerts for selected kidney and transplant journals

6. Searches of the International Clinical Trials Register (ICTRP) Search Portal and ClinicalTrials.gov.

Studies contained in the Register are identified through searches of CENTRAL, MEDLINE, and EMBASE based on the scope of Cochrane Kidney and Transplant. Details of search strategies, as well as a list of handsearched journals, conference proceedings and current awareness alerts, are available on the Cochrane Kidney and Transplant website.

See Appendix 1 for search terms used in strategies for this review.

Searching other resources

1. Reference lists of review articles, relevant studies, and clinical practice guidelines.

2. Contacting relevant individuals/organisations seeking information about unpublished or incomplete studies.

3. Grey literature sources (e.g. abstracts, dissertations, and theses), in addition to those already included in the Cochrane Kidney and Transplant Register of Studies, will not be searched.

Data collection and analysis

Selection of studies

The search strategy described will be used to obtain titles and abstracts of studies that may be relevant to the review. The titles and abstracts will be screened independently by two authors, who will discard studies that are not applicable, however studies and reviews that might include relevant data or information on studies will be retained initially. Two authors will independently assess retrieved abstracts and, if necessary, the full text, of these studies to determine which studies satisfy the inclusion criteria. Disagreements will be resolved in consultation with a third author.

Data extraction and management

Data extraction will be carried out independently by two authors using standard data extraction forms. Disagreements will be resolved in consultation with a third author. Studies reported in non‐English language journals will be translated before assessment. Where more than one publication of one study exists, reports will be grouped together and the publication with the most complete data will be used in the analyses. Where relevant outcomes are only published in earlier versions these data will be used. Any discrepancy between published versions will be highlighted.

Assessment of risk of bias in included studies

The following items will be independently assessed by two authors using the risk of bias assessment tool (Higgins 2020) (see Appendix 2).

• Was there adequate sequence generation (selection bias)?

• Was allocation adequately concealed (selection bias)?

• Was knowledge of the allocated interventions adequately prevented during the study?

  • Participants and personnel (performance bias)

  • Outcome assessors (detection bias)

• Were incomplete outcome data adequately addressed (attrition bias)?

• Are reports of the study free of suggestion of selective outcome reporting (reporting bias)?

• Was the study apparently free of other problems that could put it at a risk of bias?

Measures of treatment effect

For dichotomous outcomes (e.g. death) results will be expressed as risk ratio (RR) with 95% confidence intervals (CI). Where continuous scales of measurement are used to assess the effects of treatment (e.g. BP), the mean difference (MD) will be used, or the standardised mean difference (SMD) if different scales have been used. Where possible, we will use the mean change score from baseline. We anticipate that some studies may only report the mean endpoint score of which we will use the final time point available.

Unit of analysis issues

We will only accept randomisation of the individual participant. For multiple‐dose studies, we will use data for the first dose only. For cross‐over studies, we will use data from the first phase only.

Dealing with missing data

Any further information required from the original author will be requested by written correspondence (e.g. emailing to corresponding author/s) and any relevant information obtained in this manner will be included in the review. Evaluation of important numerical data such as screened, randomised patients as well as intention‐to‐treat, as‐treated and per‐protocol population will be carefully performed. Attrition rates, for example drop‐outs, losses to follow‐up and withdrawals will be investigated. Issues of missing data and imputation methods (for example, last‐observation‐carried‐forward) will be critically appraised (Higgins 2020).

Assessment of heterogeneity

We will first assess the heterogeneity by visual inspection of the forest plot. We will quantify statistical heterogeneity using the I² statistic, which describes the percentage of total variation across studies that is due to heterogeneity rather than sampling error (Higgins 2003). A guide to the interpretation of I² values will be as follows.

• 0% to 40%: might not be important

• 30% to 60%: may represent moderate heterogeneity

• 50% to 90%: may represent substantial heterogeneity

• 75% to 100%: considerable heterogeneity.

The importance of the observed value of I² depends on the magnitude and direction of treatment effects and the strength of evidence for heterogeneity (e.g. P‐value from the Chi² test, or a confidence interval for I²) (Higgins 2020).

Assessment of reporting biases

If possible, funnel plots will be used to assess for the potential existence of small study bias (Higgins 2020).

Data synthesis

Data will be pooled using the random‐effects model but the fixed‐effect model will also be used to ensure robustness of the model chosen and susceptibility to outliers.

Subgroup analysis and investigation of heterogeneity

Subgroup analysis will be used to explore possible sources of heterogeneity (e.g. participants, interventions, and study quality). Heterogeneity among participants could be related to age, co‐morbidities, and disease pathology. Heterogeneity in treatments could be related to prior agent(s) used and the agent, dose, and duration of therapy. Adverse effects will be tabulated and assessed with descriptive techniques, as they are likely to be different for the various agents used. Where possible, the risk difference with 95% CI will be calculated for each adverse effect, either compared to no treatment or to another agent.

Planned subgroups where sufficient data are available are as follows.

• Disease stage

• Participants with co‐morbidities

• Concurrent pharmacological medications

• Type of formulation of biotics

• Age: children, adults

• Level of gastrointestinal function of gastrointestinal issues

Sensitivity analysis

We will perform sensitivity analyses in order to explore the influence of the following factors on effect size.

• Repeating the analysis excluding unpublished studies

• Repeating the analysis taking account of risk of bias, as specified

• Repeating the analysis excluding any very long or large studies to establish how much they dominate the results

• Repeating the analysis excluding studies using the following filters: diagnostic criteria, language of publication, source of funding (industry versus other), and country.

Summary of findings and assessment of the certainty of the evidence

We will present the main results of the review in 'Summary of findings' tables. These tables present key information concerning the certainty of the evidence, the magnitude of the effects of the interventions examined, and the sum of the available data for the main outcomes (Schunemann 2020a).

The 'Summary of findings' tables also include an overall grading of the evidence related to each of the main outcomes using the GRADE (Grades of Recommendation, Assessment, Development and Evaluation) approach (GRADE 2008; GRADE 2011). The GRADE approach defines the certainty of a body of evidence as the extent to which one can be confident that an estimate of effect or association is close to the true quantity of specific interest. This will be assessed by two authors. A summary of the assessment process is in Appendix 3. The certainty of a body of evidence involves consideration of within‐trial risk of bias (methodological quality), directness of evidence, heterogeneity, precision of effect estimates and risk of publication bias (Schunemann 2020b). We plan to present the following outcomes in the 'Summary of findings' tables.

• Gastrointestinal function

• Graft health

• Adverse events

• Serious adverse events

• Death

Appendices

Appendix 1. Electronic search strategies

Database	Search terms
CENTRAL	synbiotic*:ti,ab,kw probiotic*:ti,ab,kw prebiotic*:ti,ab,kw (bifido* or lactobacill*):ti,ab,kw bacillus:ti,ab,kw enterococcus:ti,ab,kw escherichia:ti,ab,kw oligosaccharide*:ti,ab,kw saccharomyces:ti,ab,kw streptococcus next thermophilus:ti,ab,kw (resistant next starch):ti,ab,kw polydextrose:ti,ab,kw (dietary next (fiber or fibre)):ti,ab,kw "gum arabic":ti,ab,kw "hi‐maize":ti,ab,kw oligosaccharides:ti,ab,kw familact:ti,ab,kw "probinul neutro":ti,ab,kw inulin:ti,ab,kw {OR #1‐#19} ((heart or cardiac or kidney or renal or liver or hepatic or lung or pulmonary or pancrea*) near/2 (transplant* or graft* or allo*)):ti,ab,kw #20 and #21 in Trials
MEDLINE	Synbiotics/ Probiotics/ Prebiotics/ synbiotic*.tw. probiotic*.tw. prebiotic*.tw. or/1‐6 Bifidobacterium bifidum/ exp Lactobacillus/ Streptococcus Thermophilus/ exp Saccharomyces/ exp Bacillus/ exp Enterococcus/ Oligosaccarides/ Eschericia/ (bifido* or lactobacill*).tw. streptococcus thermophilus.tw. saccharomyces.tw. bacillus.tw. enterococcus.tw. eschericia.tw. Dietary Fiber/ Gum Arabic/ (dietary fiber or dietary fibre).tw. resistant starch.tw. polydextrose.tw. gum arabic.tw. hi‐maize.tw. oligosaccharides.tw. familact.tw. probinul neutro.tw. Inulin/ inulin.tw. or/8‐33 or/7,34 Organ Transplantation/ exp Heart Transplantation/ Kidney Transplantation/ Liver Transplantation/ exp Lung Transplantation/ Pancreas Transplantation/ ((heart or cardiac or kidney or renal or liver or hepatic or lung or pulmonary or pancrea*) adj2 (transplant* or graft* or allo*)).tw. or/36‐42 and/35,43
EMBASE	synbiotic agent/ prebiotic agent/ exp probiotic agent/ synbiotic*.tw. probiotic*.tw. prebiotic*.tw. or/1‐6 bifidobacterium bifidum/ exp Lactobacillus/ exp bacillus/ exp enterococcus/ escherichia/ oligosaccharide/ exp saccharomyces/ streptococcus thermophilus/ (bifido* or lactobacill*).tw. bacillus.tw. enterococcus.tw. escherichia.tw. oligosaccharide*.tw. saccharomyces.tw. streptococcus thermophilus.tw. dietary fiber/ gum arabic/ gum arabic.tw. (dietary adj (fiber or fibre)).tw. resistant starch.tw. polydextrose.tw. hi‐maize.tw. arabinoxylan oligosaccharides.tw. familact.tw. probinul neutro.tw. Inulin/ inulin.tw. or/8‐34 or/7,35 organ transplantation/ exp kidney transplantation/ exp heart transplantation/ exp liver transplantation/ exp lung transplantation/ exp pancreas transplantation/ ((heart or cardiac or kidney or renal or liver or hepatic or lung or pulmonary or pancrea*) adj2 (transplant* or graft* or allo*)).tw. or/37‐43 and/36,44

Appendix 2. Risk of bias assessment tool

Potential source of bias	Assessment criteria
Random sequence generation Selection bias (biased allocation to interventions) due to inadequate generation of a randomised sequence	Low risk of bias: Random number table; computer random number generator; coin tossing; shuffling cards or envelopes; throwing dice; drawing of lots; minimisation (minimisation may be implemented without a random element, and this is considered to be equivalent to being random).
	High risk of bias: Sequence generated by odd or even date of birth; date (or day) of admission; sequence generated by hospital or clinic record number; allocation by judgement of the clinician; by preference of the participant; based on the results of a laboratory test or a series of tests; by availability of the intervention.
	Unclear: Insufficient information about the sequence generation process to permit judgement.
Allocation concealment Selection bias (biased allocation to interventions) due to inadequate concealment of allocations prior to assignment	Low risk of bias: Randomisation method described that would not allow investigator/participant to know or influence intervention group before eligible participant entered in the study (e.g. central allocation, including telephone, web‐based, and pharmacy‐controlled, randomisation; sequentially numbered drug containers of identical appearance; sequentially numbered, opaque, sealed envelopes).
	High risk of bias: Using an open random allocation schedule (e.g. a list of random numbers); assignment envelopes were used without appropriate safeguards (e.g. if envelopes were unsealed or non‐opaque or not sequentially numbered); alternation or rotation; date of birth; case record number; any other explicitly unconcealed procedure.
	Unclear: Randomisation stated but no information on method used is available.
Blinding of participants and personnel Performance bias due to knowledge of the allocated interventions by participants and personnel during the study	Low risk of bias: No blinding or incomplete blinding, but the review authors judge that the outcome is not likely to be influenced by lack of blinding; blinding of participants and key study personnel ensured, and unlikely that the blinding could have been broken.
	High risk of bias: No blinding or incomplete blinding, and the outcome is likely to be influenced by lack of blinding; blinding of key study participants and personnel attempted, but likely that the blinding could have been broken, and the outcome is likely to be influenced by lack of blinding.
	Unclear: Insufficient information to permit judgement
Blinding of outcome assessment Detection bias due to knowledge of the allocated interventions by outcome assessors.	Low risk of bias: No blinding of outcome assessment, but the review authors judge that the outcome measurement is not likely to be influenced by lack of blinding; blinding of outcome assessment ensured, and unlikely that the blinding could have been broken.
	High risk of bias: No blinding of outcome assessment, and the outcome measurement is likely to be influenced by lack of blinding; blinding of outcome assessment, but likely that the blinding could have been broken, and the outcome measurement is likely to be influenced by lack of blinding.
	Unclear: Insufficient information to permit judgement
Incomplete outcome data Attrition bias due to amount, nature or handling of incomplete outcome data.	Low risk of bias: No missing outcome data; reasons for missing outcome data unlikely to be related to true outcome (for survival data, censoring unlikely to be introducing bias); missing outcome data balanced in numbers across intervention groups, with similar reasons for missing data across groups; for dichotomous outcome data, the proportion of missing outcomes compared with observed event risk not enough to have a clinically relevant impact on the intervention effect estimate; for continuous outcome data, plausible effect size (difference in means or standardised difference in means) among missing outcomes not enough to have a clinically relevant impact on observed effect size; missing data have been imputed using appropriate methods.
	High risk of bias: Reason for missing outcome data likely to be related to true outcome, with either imbalance in numbers or reasons for missing data across intervention groups; for dichotomous outcome data, the proportion of missing outcomes compared with observed event risk enough to induce clinically relevant bias in intervention effect estimate; for continuous outcome data, plausible effect size (difference in means or standardized difference in means) among missing outcomes enough to induce clinically relevant bias in observed effect size; ‘as‐treated’ analysis done with substantial departure of the intervention received from that assigned at randomisation; potentially inappropriate application of simple imputation.
	Unclear: Insufficient information to permit judgement
Selective reporting Reporting bias due to selective outcome reporting	Low risk of bias: The study protocol is available and all of the study’s pre‐specified (primary and secondary) outcomes that are of interest in the review have been reported in the pre‐specified way; the study protocol is not available but it is clear that the published reports include all expected outcomes, including those that were pre‐specified (convincing text of this nature may be uncommon).
	High risk of bias: Not all of the study’s pre‐specified primary outcomes have been reported; one or more primary outcomes is reported using measurements, analysis methods or subsets of the data (e.g. sub‐scales) that were not pre‐specified; one or more reported primary outcomes were not pre‐specified (unless clear justification for their reporting is provided, such as an unexpected adverse effect); one or more outcomes of interest in the review are reported incompletely so that they cannot be entered in a meta‐analysis; the study report fails to include results for a key outcome that would be expected to have been reported for such a study.
	Unclear: Insufficient information to permit judgement
Other bias Bias due to problems not covered elsewhere in the table	Low risk of bias: The study appears to be free of other sources of bias.
	High risk of bias: Had a potential source of bias related to the specific study design used; stopped early due to some data‐dependent process (including a formal‐stopping rule); had extreme baseline imbalance; has been claimed to have been fraudulent; had some other problem.
	Unclear: Insufficient information to assess whether an important risk of bias exists; insufficient rationale or evidence that an identified problem will introduce bias.

Appendix 3. The GRADE approach (Grades of Recommendation, Assessment, Development, and Evaluation)

The GRADE approach assesses the certainty of a body of evidence, rating it in one of four grades (GRADE 2008).

• High: we are very confident that the true effect lies close to that of the estimate of the effect

• Moderate: we are moderately confident in the effect estimate; the true effect is likely to be close the estimate of effect, but there is a possibility that it is substantially different

• Low: our confidence in the effect estimate is limited; the true effect may be substantially different from the estimate of the effect

• Very low: we have very little confidence in the effect estimate; the true effect is likely to be substantially different from the estimate of effect.

We decreased the certainty of evidence if there was (Balshem 2011):

• serious (‐1) or very serious (‐2) limitation in the study design or execution (risk of bias);

• important inconsistency of results (‐1);

• some (‐1) or major (‐2) uncertainty about the directness of evidence;

• imprecise or sparse data (‐1) or serious imprecision (‐2); or

• high probability of publication bias (‐1).

We increased the certainty of evidence if there was (GRADE 2011):

• a large magnitude of effect (direct evidence, relative risk (RR) = 2 to 5 or RR = 0.5 to 0.2 with no plausible confounders) (+1); very large with RR > 5 or RR < 0.2 and no serious problems with risk of bias or precision; more likely to rate up if effect is rapid and out of keeping with prior trajectory; usually supported by indirect evidence (+2);

• evidence of a dose response gradient (+1); or

• all plausible residual confounders or biases would reduce a demonstrated effect, or suggest a spurious effect when results show no effect (+1).

Contributions of authors

1. Draft the protocol: TC; NSR; JC; CH; MH; DJ; ATP; AT; GW

2. Study selection: TC; NSR

3. Extract data from studies: TC; NSR

4. Enter data into RevMan: TC; NSR

5. Carry out the analysis: TC; NSR; ATP

6. Interpret the analysis: TC; NSR; ATP

7. Draft the final review: TC; NSR; JC; CH; MH; DJ; ATP; AT; GW

8. Disagreement resolution: MH

9. Update the review: TC; GW

Sources of support

Internal sources

• No sources of support provided

External sources

• BEAT‐CKD Funding Grant 1092957, Australia

  TC and RK are employed under funding from this grant.

Declarations of interest

• TC: none known

• NSR: none known

• JC: none known

• CH: has received fees paid to her institution from Janssen and GlaxoSmithKline; Advisory Board fees paid to her from Otsuka; Research Grants to her institution from Otsuka, Shire, Fresenius, and Baxter; none of these are related to the current study. In addition, she has received grants paid to her institution from the Polycystic Kidney Disease foundation of Australia for work that is not related to the current study

• MH: none known

• DJ: has previously has previously received consultancy fees, research grants, speaker's honoraria and travel sponsorships from Baxter Healthcare and Fresenius Medical Care. He has also received consultancy fees from AstraZeneca and Awak, speaker's honoraria from Ono, and travel sponsorships from Amgen. He is a current recipient of a National Health and Research Council Practitioner Fellowship

• ATP: none known

• AT: none known

• GW: none known

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