WO2021207822A1 - Detection, treatment, and monitoring of microbiome-dependant protein fermentation metabolites - Google Patents

Abstract

Protein fermentation by the gut microbiome generates a number of metabolites, including the uremic toxins indoxyl sulfate (IS) and p-cresol sulfate (p-CS). RS consumption significantly altered the microbiome while improving several measures of protein fermentation in the gut. The use of RS by those needing to reduce the effects of protein fermentation, for example patients with chronic kidney disease, can be guided by simultaneously measuring changes in select genera in the microbiome

Description

DETECTION. TREATMENT. AND MONITORING OF MICROBIOME-DEPENDANT PROTEIN FERMENTATION METABOLITES PRIOR APPLICATION INFORMATION

The instant application claims the benefit of US Provisional Patent Application 63/009,722, filed April 14, 2020 and entitled “DETECTION, TREATMENT, AND

MONITORING OF MICROBIOME-DEPENDANT PROTEIN FERMENTATION METABOLITES”, the entire contents of which are incorporated herein by reference for all purposes. BACKGROUND OF THE INVENTION

We are only beginning to appreciate the impact the microbiota inhabiting the human intestinal tract have on our health. Collectively called the gut microbiome when measured via genomic analysis, the abundance of these bacteria varies by individual and tends to fluctuate within each person depending on diet, exercise, and other factors (Johnson et al. 2019. Cell Host Microbe). This malleable diversity is intriguing given that changes in the microbiome are associated with various human diseases, such as metabolic syndrome (Dabke et al. 2019. J Clin Invest). However, a number of complexities, including redundant genomic functions, diverse metabolic potential, and cell-to-cell interactions, make it difficult to firmly ascribe bacteria to either healthy or diseased conditions.

While the ability of the microbiome to produce various metabolites can be anticipated by genomic analysis, the utilization of those metabolic pathways depends upon numerous variables and direct microbial metabolite measurements are necessary to interpret the consequences of microbiome-mediated nutritional interventions (Oliphant and Allen-Vercoe. 2019. Microbiome). For example, most dietary proteins are cleaved into short peptides and amino acids early in digestion, and absorbed by the small intestine, but a fraction of these products pass into the large intestine where they are fermented by the microbiome. Many amino acids are deaminated and converted to short chain fatty acids (SCFAs), yielding beneficial metabolites that can be utilized by host enterocytes. However, this fermentation process also yields ammonia, a toxic substance that must be either incorporated during de novo amino acid synthesis or converted to urea by the host, and excreted via the kidneys.

Gut microbiome fermentation of tryptophan and tyrosine leads to the formation of indoxyl and p-cresol, respectively (Smith and Macfarlane. 1996. J Appl Bacteriol). Sulfotransferase enzymes in the liver then convert these molecules to indoxyl sulfate and p-cresol sulfate, toxic substances that have been implicated in pathologies (Wikoff et al. 2009. PNAS). Reducing dietary protein intake is sometimes recommended for those with impaired kidney function, in part to reduce the abundance of these toxic compounds (Gluba-Brzozka et al. 2017. Nutrients). Other research has suggested that dietary supplementation with prebiotic carbohydrates can shift the composition of the microbiome away from one that ferments proteins, thereby reducing toxic metabolite and ammonia production (Sirich et al. 2014. Clin J Am Soc Nephrol, Kieffer et al. 2016. Am J Physiol Renal Physiol, Esgalhado et al. 2018. Food Funct, Snelson et al. 2019. Adv Nutr, Khosroshahi et al. 2019. Nutr Metab (Lond)).

Flere, we report the results of a clinical trial evaluating the influence of resistant potato starch (MSPrebiotic®) on changes in the microbiome and metabolites of protein fermentation. While we demonstrate changes in protein fermentation metabolite abundance consistent with an overall shift in microbiome substrates, we show that proportional changes in different metabolites correlate differently with various genera. These results suggest that a dietary input’s effects depend upon changes in abundance of extant microbiota, which can be monitored to predict protein fermentation metabolite reduction via RS supplementation.

SUMMARY OF THE INVENTION

According to an aspect of the invention, there is provided a method for determining efficacy of a gut microbiome modulating treatment for high uremic toxins or high branched chain fatty acid (BCFA) levels in an individual at risk of developing or who has developed or who has kidney disease, said method comprising: detecting Bifidobacterium levels in a first gut microbiome sample from the individual at a first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Bifidobacterium levels in the second sample; and comparing Bifidobacterium levels in the second gut microbiome sample to Bifidobacterium levels in the first gut microbiome sample, wherein if the Bifidobacterium levels in the second sample are higher than Bifidobacterium levels in the first sample, continuing the dosage regimen for the individual.

In some embodiments, at the first time point and the second time point, at least one kidney disease related parameter of the individual is measured and these two measurements are also compared.

According to another aspect of the invention, there is provided a method for determining efficacy of a microbiome modulating treatment for high uremic toxins or high BCFA levels in an individual at risk of developing or who has developed or who has kidney disease, said method comprising: detecting Bifidobacterium levels in a first gut microbiome sample from the individual at a first time point; determining a first measurement of a kidney disease related parameter of the individual at the first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Bifidobacterium levels in the second sample; determining a second measurement of the kidney disease related parameter of the individual at the second time point; comparing Bifidobacterium levels in the second gut microbiome sample to Bifidobacterium levels in the first gut microbiome sample, and comparing the first measurement of the kidney disease related parameter and the second measurement of the kidney disease related parameter, wherein if the Bifidobacterium levels in the second sample are higher than Bifidobacterium levels in the first sample and the second kidney disease related parameter is improved compared to the first kidney disease related parameter, continuing the dosage regimen for the individual.

According to another aspect of the invention, there is provided a method for determining efficacy of a microbiome modulating treatment for high uremic toxins or high BCFA levels in an individual at risk of developing or who has developed or who has kidney disease, said method comprising: detecting Dorea and/or Blautia levels in a first gut microbiome sample from the individual at a first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Dorea and/or Blautia levels in the second sample; and comparing Dorea levels in the second gut microbiome sample to Dorea levels in the first gut microbiome sample and/or comparing Blautia levels in the second gut microbiome sample to Blautia levels in the first gut microbiome sample, wherein if the Dorea levels in the second sample are lower than Dorea levels in the first sample and/or Blautia levels in the second sample are lower than Blautia levels in the first sample, continuing the dosage regimen for the individual.

In some embodiments, at the first time point and the second time point, at least one kidney disease related parameter of the individual is measured and these two measurements are also compared.

According to another aspect of the invention, there is provided a method for determining efficacy of a microbiome modulating treatment for high uremic toxins or high BCFA levels in an individual at risk of developing or who has developed or who has kidney disease, said method comprising: detecting Dorea and/or Blautia levels in a first gut microbiome sample from the individual at a first time point; determining a first measurement of a kidney disease related parameter of the individual at the first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Dorea and/or Blautia levels in the second sample; determining a second measurement of the kidney disease related parameter of the individual at the second time point; comparing Dorea levels in the second gut microbiome sample to Dorea levels in the first gut microbiome sample and/or comparing Blautia levels in the second gut microbiome sample to Blautia levels in the first gut microbiome sample, and comparing the first measurement of the kidney disease related parameter and the second measurement of the kidney disease related parameter, wherein if the Dorea levels in the second sample are lower than Dorea levels in the first sample and/or Blautia levels in the second sample are lower than Blautia levels in the first sample, and the second kidney disease related parameter is improved compared to the first kidney disease related parameter, continuing the dosage regimen for the individual.

In some embodiments, the gut microbiome sample is, for example but by no means limited to, a stool or fecal sample or colonic contents, whether sampled in situ or via intervention. In some embodiments, the microbiome modulating treatment is a microbiome therapy, that is, a treatment that is known to or expected to alter the microbiome of the individual. Examples of microbiome therapies are discussed herein and other examples will be readily apparent to one of skill in the art.

According to another aspect of the invention, there is provided a method for detecting the signature of an altered gut microbiome (also known as dysbiosis) that is correlated with impaired uremic toxin and/or BCFA homeostasis in an individual comprising the monitoring of Bifidobacterium, Dorea, and/or Blautia levels and predicting the efficacy of microbiome therapies if Bifidobacterium, Dorea, and/or Blautia are present.

As will be appreciated by one of skill in the art, Bifidobacterium, Dorea, and/or Blautia may be detected in a sample by a variety of means, which will be readily apparent to one of skill in the art. Illustrative examples are provided below.

In some embodiments of the invention, Bifidobacterium, Dorea, and/or Blautia is detected by directed 16S V4 ribosomal subunit amplification (for example, Real-Time Polymerase Chain Reaction; RT-PCR or Quantitative PCR; qPCR) of Bifidobacterium, Dorea, and/or Blautia using the abundance of Bacteroides or other common commensal unrelated to blood uremic toxin or BCFA regulation as the reference value. As will be apparent to one of skill in the art, Bacteroides is both common (found in most gut microbiomes) and abundant (making up a large proportion of each microbiome), and accordingly is suitable to be used as an internal control. However, other suitable candidates for use as an internal control will be readily apparent to one of skill in the art. In another embodiment of the invention, Bifidobacterium, Dorea, and/or Blautia is detected by whole microbiome sequencing using the 16S V4 ribosomal subunit and/or other relevant regions.

In another embodiment of the invention, Bifidobacterium, Dorea, and/or Blautia is detected by shotgun metagenome sequencing, or another suitable unbiased genomic- based approach, or any method that reports proportional representation of Bifidobacterium, Dorea, and/or Blautia in the microbiome.

In some embodiments of the invention, the individual has high free indoxyl sulfate (about 3.0 mg/L or higher in serum, or as determined or diagnosed by a physician) and/or high p-cresol sulfate (about 1.0 mg/L or higher in serum, or as determined or diagnosed by a physician) and/or high BCFA levels (about 0.3 mmol/kg isobutyrate or higher and/or 0.5 mmol/kg isovalerate or higher in stool) and/or high urea (blood urea nitrogen; about 7.0 mmol/L) or higher, or as determined or diagnosed by a physician).

In another embodiment of the invention, the individual is at risk of developing chronic kidney disease (CKD) due to family history, lifestyle factors, or due to co- morbidities such as diabetes or metabolic syndrome. In another embodiment of the invention, the individual has impaired renal function (an estimated glomerular filtration rate of 60 ml_/min/1 .73 m² or less, or creatinine levels of 100 micromol/L).

In another embodiment of the invention, the individual has been diagnosed with or is suspected of having kidney disease.

In some embodiments of the invention, the microbiome therapy is a prebiotic, administered daily or as needed, for as long as the kidney markers continue to show improvement compared to baseline levels.

As discussed herein, the prebiotic microbiome therapeutic may be digestion resistant starch from potatoes or resistant potato starch, delivered daily or as needed, for as long as the kidney disease markers continue to show improvement compared to baseline levels.

As discussed herein, the effective amount may be for example 2 to 40 g, 2 to 30 g, 2 to 20 g, 5 to 40 g, 5 to 30 g, 5 g to 20 g, or 10 to 20 g of resistant potato starch.

In another embodiment of the invention, the microbiome therapy is a probiotic, administered daily or as needed, for as long as the kidney disease markers continue to show improvement compared to baseline levels.

In another embodiment of the invention, the microbiome therapy is an antibiotic, administered daily or as needed, for as long as the kidney disease markers continue to show improvement compared to baseline levels.

In another embodiment of the invention, the microbiome therapy is a combination of prebiotics, and/or probiotics, and/or antibiotics, and/or bacteriophages, administered daily or as needed, for as long as the kidney disease markers continue to show improvement compared to baseline levels.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1. Baseline Bifidobacterium levels in both RS and placebo groups were comparable to levels normally found in a typical ‘Western’ gut microbiome. Consumption of RS led to a significant increase in Bifidobacterium levels while the placebo had no effect. Data indicated are means +/- SEM. ^* p = 0.029293. Figure 2. Consumption of RS led to a significant decrease in indoxyl sulfate levels while the placebo had no effect. Data indicated are means +/- SEM. ^* p = 0.039776.

Figure 3. Treatments had different effects on BCFA abundance. A. RS consumption led to a significant decrease in isobutyrate levels (^* p = 0.045693). B. Consumption of the placebo led to significant reductions in isovalerate (# p = 0.022368). Data indicated are means +/- SEM.

Figure 4. Pathways for amino acid metabolism into BCFAs by Blautia (A) and Tryptophan metabolism into N-Acetylisatin by Bifidobacterium (B), modified from the Kyoto Encyclopedia of Genes and Genomes (KEGG; Kanehisa and Goto. 2000. Nucleic Acids Res).

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are now described. All publications mentioned hereunder are incorporated herein by reference.

We investigated the correlation between improvements in kidney disease-related parameters and changes in the microbiome in response to supplementation with prebiotic resistant potato starch (MSPrebiotic®).

As discussed herein, protein fermentation by the gut microbiome generates a number of metabolites, including the uremic toxins indoxyl sulfate (IS) and p-cresol sulfate (p-CS). Prebiotic supplementation is thought to decrease protein fermentation metabolites, though the relationship between changes in these metabolites and changes in the microbiome remain poorly understood.

The primary objective was to determine the impact of daily consumption of prebiotic RS (MSPrebiotic®) or placebo on the microbiome of healthy adults over three months. Secondary objectives included assessment of branched-chain fatty acids (BCFAs) in stool, as well as levels of uremic toxins, blood glucose, and insulin in serum as measures of host response to changes in the gut microbiome. As discussed below, this was a prospective, placebo controlled, randomized, double-blinded study. Samples were collected at enrollment or baseline, and 14 weeks after randomization to placebo or RS. Microbiome analysis was done using 16S rRNA sequencing of DNA extracted from stool. BCFA analysis was performed using gas chromatography while uremic toxins were measured by mass spectroscopy.

Specifically, eighty-one participants were randomized to either placebo (37) or RS (44) who completed the study. RS consumption led to an increase in Bifidobacterium abundance (p = 0.03) after 12 weeks of consumption. IS levels were significantly decreased (p = 0.04) and increases in Bifidobacterium were correlated with decreases in both IS (p = 0.002) and p-CS (p = 0.03). Isobutyrate levels decreased in response to RS (p = 0.045), and correlation analysis revealed positive relationships between isobutyrate and isovalerate and Dorea and Blautia (p < 0.00007 for each genus-BCFA correlation).

As discussed below, RS consumption significantly altered the microbiome while improving several measures of protein fermentation in the gut. The use of RS by those needing to reduce the effects of protein fermentation, for example patients with chronic kidney disease, can be guided by simultaneously measuring changes in select genera in the microbiome

MSP Starch Products Inc. manufactures MSPrebiotic® Resistant Potato Starch, an unmodified type 2 resistant starch (RS2) that is a Solanum tuberosum preparation of food grade quality for animal and human food application. Resistant potato starch is also referred to as digestion or digestive resistant starch, or simply resistant starch (RS). While MSPrebiotic®, which contains 7 g of fiber in 10 g of product is used in the trials and experiments discussed herein, it is important to note that as discussed herein, another suitable resistant potato starch or potato resistant starch, that is, another unmodified RS type 2 potato starch, comprising at least 60% resistant starch or at least 65% resistant starch or at least 70% resistant starch or at least 75% resistant starch or at least 80% resistant starch of total extract or total potato extract may be used. That is, the extract itself may comprise at least 60% resistant starch, at least 65% resistant starch, at least 70% resistant starch, at least 75% resistant starch or at least 80% resistant starch on a weight to weight basis. According to an aspect of the invention, there is provided a method for determining efficacy of a gut microbiome modulating treatment for high uremic toxins or high branched chain fatty acid (BCFA) levels in an individual at risk of developing or who has developed or who has kidney disease, said method comprising: detecting Bifidobacterium levels in a first gut microbiome sample from the individual at a first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Bifidobacterium levels in the second sample; and comparing Bifidobacterium levels in the second gut microbiome sample to Bifidobacterium levels in the first gut microbiome sample, wherein if the Bifidobacterium levels in the second sample are higher than Bifidobacterium levels in the first sample, continuing the dosage regimen for the individual.

In some embodiments, at the first time point and the second time point, at least one kidney disease related parameter of the individual is measured and these two measurements are also compared.

According to another aspect of the invention, there is provided a method for determining efficacy of a microbiome modulating treatment for high uremic toxins or high BCFA levels in an individual at risk of developing or who has developed or who has kidney disease, said method comprising: detecting Dorea and/or Blautia levels in a first gut microbiome sample from the individual at a first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Dorea and/or Blautia levels in the second sample; and comparing Dorea levels in the second gut microbiome sample to Dorea levels in the first gut microbiome sample and/or comparing Blautia levels in the second gut microbiome sample to Blautia levels in the first gut microbiome sample, wherein if the Dorea levels in the second sample are lower than Dorea levels in the first sample and/or Blautia levels in the second sample are lower than Blautia levels in the first sample, continuing the dosage regimen for the individual.

In some embodiments, at the first time point and the second time point, at least one kidney disease related parameter of the individual is measured and these two measurements are also compared. According to another aspect of the invention, there is provided a method for determining efficacy of a microbiome modulating treatment for high uremic toxins or high BCFA levels in an individual at risk of developing or who has developed or who has kidney disease, said method comprising: detecting Bifidobacterium levels in a first gut microbiome sample from the individual at a first time point; determining a first measurement of a kidney disease related parameter of the individual at the first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Bifidobacterium levels in the second sample; determining a second measurement of the kidney disease related parameter of the individual at the second time point; comparing Bifidobacterium levels in the second gut microbiome sample to

Bifidobacterium levels in the first gut microbiome sample, and comparing the first measurement of the kidney disease related parameter and the second measurement of the kidney disease related parameter, wherein if the Bifidobacterium levels in the second sample are higher than Bifidobacterium levels in the first sample and the second kidney disease related parameter is improved compared to the first kidney disease related parameter, continuing the dosage regimen for the individual.

It is of note that while it may be more convenient to obtain samples for Bifidobacterium and kidney disease-related parameter measurements at the same time, this is not a requirement of the invention. That is, the samples do not necessarily need to be taken at exactly the same time, but may be taken separately within a reasonable time period and still be considered as having been taken at either the first time point or the second time point as the case may be.

Similarly, the measuring of the samples does not need to be done immediately or even by the same institution. That is, means for storing suitable samples for measurement of bacterial levels or kidney disease-related parameters are well known in the art.

The individual who is at risk of developing kidney disease may be at risk based on genetic predisposition, familial history, heredity, lifestyle and/or one or more kidney disease-related parameters being out of range, for example, elevated serum levels of uremic toxins indoxyl sulfate and/or p-cresol sulfate, elevated urea and/or creatinine levels, and/or altered estimated glomerular filtration rates. As discussed above, the individual may also be an individual who has kidney disease, that is, an individual who has been diagnosed with kidney disease. Similarly, the individual may be an individual who has developed kidney disease, that is, an individual who has recently developed kidney disease and who may or may not have been diagnosed with kidney disease.

According to another aspect of the invention, there is provided a method for determining efficacy of a microbiome modulating treatment for high uremic toxins or high BCFA levels in an individual at risk of developing or who has developed or who has kidney disease, said method comprising: detecting Dorea and/or Blautia levels in a first gut microbiome sample from the individual at a first time point; determining a first measurement of a kidney disease related parameter of the individual at the first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Dorea and/or Blautia levels in the second sample; determining a second measurement of the kidney disease related parameter of the individual at the second time point; comparing Dorea levels in the second gut microbiome sample to Dorea levels in the first gut microbiome sample and/or comparing Blautia levels in the second gut microbiome sample to Blautia levels in the first gut microbiome sample, and comparing the first measurement of the kidney disease related parameter and the second measurement of the kidney disease related parameter, wherein if the Dorea levels in the second sample are lower than Dorea levels in the first sample and/or Blautia levels in the second sample are lower than Blautia levels in the first sample, and the second kidney disease related parameter is improved compared to the first kidney disease related parameter, continuing the dosage regimen for the individual.

It is of note that while it may be more convenient to obtain samples for Dorea and/or Blautia and kidney disease-related parameter measurements at the same time, this is not a requirement of the invention. That is, the samples do not necessarily need to be taken at exactly the same time, but may be taken separately within a reasonable time period and still be considered as having been taken at either the first time point or the second time point as the case may be.

Similarly, the measuring of the samples does not need to be done immediately or even by the same institution. That is, means for storing suitable samples for measurement of bacterial levels or kidney disease-related parameters are well known in the art. The individual who is at risk of developing kidney disease may be at risk based on genetic predisposition, familial history, heredity, lifestyle and/or one or more kidney disease-related parameters being out of range, for example, elevated serum levels of uremic toxins indoxyl sulfate and/or p-cresol sulfate, elevated urea and/or creatinine levels, and/or altered estimated glomerular filtration rates. As discussed above, the individual may also be an individual who has kidney disease, that is, an individual who has been diagnosed with kidney disease. Similarly, the individual may be an individual who has developed kidney disease, that is, an individual who has recently developed kidney disease and who may or may not have been diagnosed with kidney disease.

According to an aspect of the invention, there is provided a method for determining efficacy of a gut microbiome modulating treatment for high uremic toxins or high branched chain fatty acid (BCFA) levels in an individual at risk of developing or who has developed or who has kidney disease, said method comprising: detecting Bifidobacterium levels in a first gut microbiome sample from the individual at a first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Bifidobacterium levels in the second sample; and comparing Bifidobacterium levels in the second gut microbiome sample to Bifidobacterium levels in the first gut microbiome sample, wherein if the Bifidobacterium levels in the second sample are higher than Bifidobacterium levels in the first sample, the microbiome modulating treatment is effective. If this is the case, the treatment, that is, the dosage regimen, is continued.

According to another aspect of the invention, there is provided a method for determining efficacy of a microbiome modulating treatment for high uremic toxins or high BCFA levels in an individual at risk of developing or who has developed or who has kidney disease, said method comprising: detecting Dorea and/or Blautia levels in a first gut microbiome sample from the individual at a first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Dorea and/or Blautia levels in the second sample; and comparing Dorea levels in the second gut microbiome sample to Dorea levels in the first gut microbiome sample and/or comparing Blautia levels in the second gut microbiome sample to Blautia levels in the first gut microbiome sample, wherein if the Dorea levels in the second sample are lower than Dorea levels in the first sample and/or Blautia levels in the second sample are lower than Blautia levels in the first sample, the microbiome modulating treatment is effective. If this is the case, the treatment, that is, the dosage regimen, is continued.

According to another aspect of the invention, there is provided a method for determining efficacy of a microbiome modulating treatment for high uremic toxins or high BCFA levels in an individual at risk of developing or who has developed or who has kidney disease, said method comprising: detecting Bifidobacterium levels in a first gut microbiome sample from the individual at a first time point; determining a first measurement of a kidney disease related parameter of the individual at the first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Bifidobacterium levels in the second sample; determining a second measurement of the kidney disease related parameter of the individual at the second time point; comparing Bifidobacterium levels in the second gut microbiome sample to Bifidobacterium levels in the first gut microbiome sample, and comparing the first measurement of the kidney disease related parameter and the second measurement of the kidney disease related parameter, wherein if the Bifidobacterium levels in the second sample are higher than Bifidobacterium levels in the first sample and the second kidney disease related parameter is improved compared to the first kidney disease related parameter, the gut microbiome modulating treatment is effective. If that is the case, then the treatment, that is, the dosage regimen, is continued. As will be appreciated by one of skill in the art, during the suitable period of time as defined above, the individual continues to be administered the gut microbiome modulating treatment.

According to another aspect of the invention, there is provided a method for determining efficacy of a microbiome modulating treatment for high uremic toxins or high BCFA levels in an individual at risk of developing or who has developed or who has kidney disease, said method comprising: detecting Dorea and/or Blautia levels in a first gut microbiome sample from the individual at a first time point; determining a first measurement of a kidney disease related parameter of the individual at the first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Dorea and/or Blautia levels in the second sample; determining a second measurement of the kidney disease related parameter of the individual at the second time point; comparing Dorea levels in the second gut microbiome sample to Dorea levels in the first gut microbiome sample and/or comparing Blautia levels in the second gut microbiome sample to Blautia levels in the first gut microbiome sample, and comparing the first measurement of the kidney disease related parameter and the second measurement of the kidney disease related parameter, wherein if the Dorea levels in the second sample are lower than Dorea levels in the first sample and/or Blautia levels in the second sample are lower than Blautia levels in the first sample, and the second kidney disease related parameter is improved compared to the first kidney disease related parameter, the gut microbiome modulating treatment is effective. If that is the case, then the treatment, that is, the dosage regimen, is continued. As will be appreciated by one of skill in the art, during the suitable period of time as defined above, the individual continues to be administered the gut microbiome modulating treatment.

As discussed herein, we demonstrate a method for detecting and treating individuals with impaired kidney homeostasis who are sensitive to microbiome-targeted therapeutic intervention using a microbiome modulating compound. In some embodiments, the microbiome modulating compound is prebiotic resistant potato starch.

In other embodiments, the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; and galactomannan polysaccharides.

Dietary changes that support the growth of healthy bacteria, including the probiotic bacteria listed above:

Dietary treatments that increase the availability of microbiota-accessible carbohydrates (MACs), for example prebiotics, to Bifidobacterium, including those prebiotics listed above.

Dietary treatments that reduce the availability of protein and/or peptides and/or amino acids and/or other fermentation substrates to Dorea and/or Blautia in the digestive tract.

Antibiotics that target Dorea and/or Blautia or another bacterium/other bacteria that facilitate the growth of Dorea and/or Blautia.

The probiotic genera, species and strains may be selected from the group consisting of: Bifidobacterium; Staphylococcus; Clostridium; Lactobacilli; Prevotella; Barnsiella; Parasutterella; and combinations thereof;

The resistant starch may be RS1 , RS2, RS3, RS4, or RS5.

The corn may be high amylose maize.

The grains may be barley, wheat, sorghum, oats or the like. Examples of suitable fructooligosaccharides include but are by no means limited to inulin and inulin-type fructans.

The galactooligosaccharides may be of varying lengths, for example, between 2 and 8 saccharide units, and may include various linkages of galactose for example but by no means limited to b-(1 -4), b-(1-6) galactose, and a terminal glucose.

The Xylooligosaccharides may be composed of xylose or related C5 sugar oligosaccharides.

The mannanoligosaccharides, may be for example glucomannanoligosaccharides.

Suitable galactomannan polysaccharides include guar gum.

In other embodiments, the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that reduce the availability of protein and/or peptides and/or amino acids and/or other fermentation substrates to Dorea and/or Blautia in the digestive tract; dietary treatments that increase the availability of microbiota-accessible carbohydrates (MACs) and/or prebiotics and/or other fermentation substrates to Bifidobacterium in the digestive tract; and antibiotics that target Dorea and/or Blautia or another bacterium/other bacteria that facilitate the growth of Dorea and/or Blautia.

In yet other embodiments, the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that reduce the availability of protein and/or peptides and/or amino acids and/or other fermentation substrates to Dorea and/or Blautia in the digestive tract; antibiotics that target Dorea and/or Blautia or another bacterium/other bacteria that facilitate the growth of Dorea and/or Blautia ; mixed plant cell wall fibers; beta-glucans; resistant dextrins; resistant maltodextrins; limit dextrins; polydextrose; alginate; pectin polysaccharides; hydroxypropylmethylcellulose; chitin; chondroitin-containing compounds; and glucosamine-containing compounds.

Preferably, the mixed plant cell wall fibers comprise two or more of the following plant cell wall fibers in varying proportions: cellulose, pectin, lignin, beta-glucan, and arabinoxylan regardless of source.

The Beta-glucans may be from cereal, such as for example, mixed-link (1 -3, 1 -4) beta-glucans from oat, barley, rye, wheat, or the like, or from fungal sources, for example, yeast, mushroom, and the like.

Resistant dextrins, resistant maltodextrins, and limit dextrins may be from wheat, corn, or other suitable sources. These non-digestible oligosaccharides of glucose molecules are joined by digestible linkages and non-digestible a-1 ,2 and a-1 ,3 linkages.

The polydextrose may be highly branched and may contain a- and b- 1 -2, 1 -3, 1 - 4 and 1 -6 linkages, with the 1 -6 linkage predominating in the polymer.

The alginate may be b-1 ,4-D-mannuronic acid and a-1 ,4-L-guluronic acid organized in homopolymeric compounds of either mannuronate or guluronate, or as heteropolymeric compounds, expressed as mannuronic acid to guluronic acid ratio.

The pectin polysaccharides may have a backbone chain of a- (1® 4)-linked D- galacturonic acid units interrupted by the insertion of (1 ® 2)-linked L-rhamnopyranosyl residues in adjacent or alternate positions. These compounds are present in cell walls and intracellular tissues of fruits, vegetables, legumes, and nuts.

Hydroxypropylmethylcellulose, also known as Hypromellose, is a propylene glycol ether of methylcellulose containing methoxyl groups and hydroxypropyl group.

The chitin may be from for example from fungi or arthropods.

Suitable chondroitin-containing compounds includes chondroitin sulfate from animal sources.

Suitable glucosamine-containing compounds includes glucosamine sulfate from animal sources. In some embodiments, the gut microbiome modulating treatment may be or may also include spores from a single strain or specie of bacteria, yeast, or other fungi; bacteriophage or a combination of bacteriophages; or an exogenously produced metabolite or metabolites normally derived from the metabolism of the gut microbiome, also known as postbiotics or parabiotics.

As will be appreciated by one of skill in the art, a kidney disease-related parameter as used herein refers to a parameter that is associated with or measured as part of monitoring kidney function.

In some embodiments of the invention, the kidney disease-related parameter is selected from the group consisting of: serum and/or blood indoxyl sulfate levels; stool indoxyl levels; serum and/or blood p-cresol sulfate levels; stool p-cresol levels; stool isobutyrate levels; stool isovalerate levels; serum and/or blood ammonia levels; serum and/or blood urea levels; urea levels in urine; Kt/V values; urea reduction ratios; serum and/or blood urea nitrogen levels; serum and/or blood creatinine levels; creatinine levels in urine; serum and/or blood albumin levels; serum and/or blood protein levels; protein levels in urine; estimated glomerular filtration rates.

As discussed herein, high free indoxyl sulfate in serum is generally diagnosed at about 3.0 mg/L or higher; high p-cresol sulfate in serum is generally diagnosed at about 1.0 mg/L or higher; high isobutyrate in stool is generally diagnosed at about 0.3 mmol/kg or higher; high isovalerate in stool is generally diagnosed at about 0.5 mmol/kg or higher; high blood urea nitrogen is generally diagnosed at about 8.0 mmol/L or higher; impaired glomerular filtration is generally diagnosed at a rate of about 60 mL/min/1.73 m² or less; high urine urea is generally diagnosed at 20 g or more urea excreted within a 24 hour period; high blood creatinine is generally diagnosed at about 100 micromol/L or higher; high urine creatinine is generally diagnosed at about 17 mmol/day or higher; high protein in blood is generally diagnosed at about 80g/L or higher, high urine protein is generally diagnosed at about 0.15 g/day or higher; high albumin in blood is generally diagnosed at 50g/L or higher; and high blood ammonia is generally diagnosed at about 45 micrograms/dL or higher. In another embodiment of the invention, the individual is at risk of developing chronic kidney disease (CKD) due to family history, lifestyle factors, or due to co morbidities such as diabetes or metabolic syndrome.

In another embodiment of the invention, the individual has impaired renal function (an estimated glomerular filtration rate of 60 ml_/min/1.73 m² or less, or creatinine levels of 100 micromol/L).

The period of time, that is, the suitable period of time may be for example about 1 week, about 2 weeks, about 3 weeks, about 4 weeks, about 5 weeks, about 6 weeks, about 7 weeks, about 8 weeks, about 9 weeks, about 10 weeks, about 11 weeks, about 12 weeks or longer. As will be appreciated by one of skill in the art, a “dosage regimen” will comprise taking an effective amount of the treatment for the duration of the suitable period of time, as discussed herein.

Bifidobacterium, Blautia, and/or Dorea levels may be measured using any suitable means known in the art. For example, levels of these bacteria may be measured using real-time polymerase chain reaction (RT-PCR)-based methods; qualitative PCR (qPCR) based methods; by microbiome sequencing directed at any sequence that defines Bifidobacterium, Blautia, or Dorea, including but not limited to the 16S V4 ribosomal subunit sequence; by shotgun metagenomic sequencing; by quantitative fluorescent in situ hybridization (FISH) with probes recognizing sequence that defines Bifidobacterium, Blautia, or Dorea, including but not limited to the 16S V4 ribosomal subunit sequence; or by antibody or cell-binding based methods.

As will be appreciated by one of skill in the art, the bacterial levels are being measured over time. Consequently, levels of bacteria may be determined by direct measurement, using suitable means known in the art, for example, such as those discussed above. Alternatively, the level of Bifidobacterium, Blautia, and/or Dorea in a given sample may be compared to an internal control, for example, using the abundance of Bacteroides or other common commensal unrelated to blood glucose or insulin regulation as the reference value. As will be apparent to one of skill in the art, Bacteroides is both common (found in most gut microbiomes) and abundant (making up a large proportion of each microbiome), and accordingly is suitable to be used as an internal control. However, other suitable candidates for use as an internal control will be readily apparent to one of skill in the art. Alternatively, the control may be a non-biological control. Furthermore, as will be appreciated by one of skill in the art, the control does not necessarily need to be repeated with each measurement.

As will be apparent to those of skill in the art, an “effective amount” of a gut microbiome modulating compound is an amount that is believed to be sufficient to reduce Blautia and/or Dorea levels, and/or increase Bifidobacterium, and improve at least one kidney disease-related parameter in the individual when administered on a dosage regimen or schedule over the suitable period of time. Such an effective amount will of course depend on the specific gut microbiome modulating compound being administered as well as other factors such as the age, weight, general condition and severity of symptoms of the individual.

As discussed herein, the prebiotic microbiome therapeutic may be resistant potato starch, delivered daily or as needed, for as long as the metabolic markers continue to show improvement compared to baseline levels.

As discussed herein, the effective amount of resistant potato starch may be for example 2 to 40 g, 2 to 30 g, 2 to 20 g, 5 to 40 g, 5 to 30 g, 5 g to 20 g, or 10 to 20 g of resistant potato starch.

The effective amount may be administered in one or more doses during the day.

As used herein, “daily” does not necessarily mean “every day” but may mean 9 out 10 days; 8 out of 9 days; 7 out of 8 days; 6 out of 7 days; 5 out of 6 days; 4 out of 5 days; 3 out of 4 days; 2 out of 3 days; 1 out of 2 days or combinations thereof.

Specifically, we measured the correlations between improvements in kidney disease-related parameters, including changes in serum uremic toxins indoxyl sulfate and p-cresol sulfate, and changes in stool BCFAs isobutyrate and isovalerate, and changes in genera in response to supplementation with prebiotic resistant potato starch.

Microbiome changes in response to RS consumption were previously investigated and significant increases in Bifidobacterium were documented (Alfa et al. 2018. Clin Nutr). Flere, we found that RS supplementation led to a robust three-fold increase in Bifidobacterium while the placebo had no effect (Figure 1 ).

Previous research has shown that RS consumption decreases blood glucose levels in the elderly (Alfa et al. 2018. Front Med). Flere, we found that RS consumption significantly reduced blood glucose levels by 2% in the elderly cohort but had no effect on blood glucose in the middle aged (Table 1). Previous improvements in blood glucose levels were correlated with decreases in Sporacetigenium (Bush and Alfa. 2018. JARCP). While the abundance of this genus was not reported in this study, precluding confirmation of this correlation, the influence of RS on blood glucose and the bifidogenic effects are consistent with previous findings (Alfa et al. 2018. Front Med; Alfa et al. 2018. Clin Nutr).

The gut microbiome influences host health through the production of metabolites (reviewed in Oliphant and Allen-Vercoe. 2019. Microbiome). Indoxyl sulfate and p-cresol sulfate are liver-modified metabolites derived from gut microbiome fermentation of tryptophan and tyrosine, respectively, and others have shown that resistant starches (RS) can have variable effects on the production of these toxins (Sirich et al. 2014. Clin J Am Soc Nephrol, Kieffer et al. 2016. Am J Physiol Renal Physiol, Esgalhado et al. 2018. Food Funct, Snelson et al. 2019. Adv Nutr, Khosroshahi et al. 2019. Nutr Metab (Lond)). While RS consumption had no effect overall on p-cresol sulfate (data not shown), indoxyl sulfate levels were significantly reduced in participants consuming RS but not placebo (Figure 2), suggesting that RS fermentation in the microbiome may affect the production uremic toxins.

We previously demonstrated that improvements in blood glucose and insulin levels following RS consumption are correlated with changes in a specific genus in the gut microbiome (Bush and Alfa. 2018. JARCP). We therefore asked whether RS-dependent decreases in indoxyl sulfate levels were correlated with changes in abundance for the forty-five genera identified in our gut microbiome analysis by calculating Pearson correlation coefficients. Correlations were ranked by p value and each p value was compared to the critical value, starting with the lowest-ranking correlation to correct for multiple testing at a false discovery rate of 0.1 (Benjamini and Flochberg. 1995. J R Stat Soc B; Table 2). A negative correlation between the decrease in indoxyl sulfate and increase in Bifidobacterium was the only correlation to remain significant after multiple testing correction (r = -0.463355; p = 0.001538; Table 2).

While p-cresol levels overall did not change in response to RS consumption, levels in some participants improved, so we asked whether changes in Bifidobacterium were also correlated with changes in p-cresol sulfate. Increases in Bifidobacterium were significantly correlated with decreases in p-cresol sulfate in people consuming RS (r= - 0.327496; p = 0.030005). No significant correlations between Bifidobacterium and indoxyl sulfate (r= -0.064733608; p = 0.706678) or p-cresol sulfate (r= -0.134385937; p = 0.429125) were identified in participants consuming the placebo. Indoxyl sulfate and p-cresol sulfate precursors and branched chain fatty acids

(BCFAs) are all produced by protein fermentation in the large intestine. We therefore measured BCFA levels in response to RS consumption and found that RS significantly decreased isovalerate levels (Figure 3A). We then asked whether RS-dependent decreases in isobutyrate were correlated with changes in bacterial abundance by calculating Pearson correlation coefficients and then correcting for multiple testing (Table 3). Positive correlations between changes in isobutyrate and both Dorea (r= 0.57423; p = 0.00005) and Blautia (r= 0.56556; p = 0.00006) remained significant after correction. Given that the placebo decreased isovalerate (Figure 3B), we examined correlations between changes Dorea and Blautia and changes in BCFAs in both treatment groups and found that correlations between both Dorea and Blautia and BCFAs are more robust and display stronger pvalues in the RS group compared to the placebo (Table 4). Neither RS nor placebo consumption had significant effects on the levels of either Dorea or Blautia.

Analyses of these data replicated discoveries from a previous trial while uncovering novel relationships between RS consumption, changes in the microbiome, and changes in metabolites produced by the microbiome. Consistent with previous findings (Alfa et al. 2018. Front Med, Alfa et al. 2018. Clin Nutr), we documented a significant decrease in blood glucose levels in elderly participants after RS consumption and found that RS supplementation led to significant increases in Bifidobacterium in the general population.

Novel investigations involved the measurement of uremic toxins and BCFAs, both metabolites of microbial origin that arise from protein fermentation in the gut (Oliphant and Allen-Vercoe. 2019. Microbiome). Previous studies have demonstrated that microbiome protein fermentation can be modulated by dietary inputs (Geypens et al. 1997. Gut) and while recommendations to patients with compromised kidney function often include shifting the diet away from protein towards increased fiber, supplementation with high amylose maize resistant starch has had inconsistent effects on uremic toxins (Sirich et al. 2014. Clin J Am Soc Nephrol, Kieffer et al. 2016. Am J Physiol Renal Physiol, Esgalhado et al. 2018. Food Funct, Snelson et al. 2019. Adv Nutr, Khosroshahi et al. 2019. Nutr Metab (Lond)). Similarly, RS fermentation by the gut microbiome has varied effects on BCFAs (Sharp and McFarlane. 2000. Appl Environ. Microbiol, Willig et al. 2005.

J Agric Food Chem, Fleo et al. 2014. J Anim Sci, Beloshapka et al. 2014. J Nutr Sci, Fie et al. 2017. Anim Nutr). Flere, we identify correlations between improvements in serum levels of indoxyl sulfate and p-cresol sulfate and Bifidobacterium, and correlations between lower levels of isovalerate and isobutyrate in stool and changes in Dorea and Blautia in response to RS supplementation. Our findings suggest that the metabolite response to RS supplementation can be predicted by the composition of the microbiome.

A lack of consensus between biomarkers predicting changes in serum uremic toxins and protein fermentation in stool is not unexpected for several reasons. First, each metabolite is derived from different amino acids, each with different catabolic fermentation pathways (Oliphant and Allen-Vercoe. 2019. Microbiome). For example, leucine and valine are converted to isovalerate and isobutyrate, respectively, via Stickland reactions (Nisman. 1954. Bacteriology Reviews). Protein fermentation in the gut generally produces toxic ammonia and carbon dioxide, along with SCFAs that can be used energy by enterocytes. Flowever, fermentation via Stickland reactions produces BCFAs instead of SCFAs. While there is some evidence to suggest BCFAs can substitute for SCFAs during stress, the production of these metabolites is generally considered to correlate with protein fermentation (Cires et al. 2019. Food Funct).

Gut microbiome fermentation of tryptophan and tyrosine leads to the formation of indoxyl and p-cresol, respectively, which are then converted to indoxyl sulfate and p- cresol sulfate (Smith and Macfarlane. 1996. J Appl Bacteriol, Wikoff et al. 2009. PNAS). These substances have been implicated in pathologies, namely chronic kidney disease (Wing et al. 2016. Exp Physiol).

Second, the correlations identified likely underlie the different metabolic functions of these genera. Given that production of both isovalerate and isobutyrate occurs via Stickland reactions in various Clostridia (Nisman. 1954. Bacteriology Reviews, Oliphant and Allen-Vercoe. 2019. Microbiome), it is not surprising that changes in the levels of these BCFAs display similar correlations with changes in bacteria in response to RS supplementation. Roles for Blautia and Dorea in the production of BCFAs have not been published, but both belong to family Lachnospiraceae of the class Clostridia and the positive correlations presented here suggest that decreases in these genera may lower the microbiome’s capacity to produce BCFAs. While not wishing to be bound to a specific theory or hypothesis, metabolic pathway analysis shows Blautia can metabolize amino acids into BCFAs using coupled deamination and reduction consistent with Stickland fermentation (Figure 4A; Kanehisa and Goto. 2000. Nucleic Acids Res). The Dorea genome, however, has not been mapped for metabolic potential. The observation that overall levels of Blautia and Dorea did not significantly change in response to RS supplementation suggest that monitoring the levels of these bacteria is necessary to predict the microbiome’s ability to produce BCFAs. While BCFAs themselves are not known to be noxious, their production is coupled with toxic ammonia production and the generation of urea, which must be filtered from the blood via the kidneys, suggesting that strategies to mitigate BCFA production will similarly reduce ammonia and urea production, thereby benefiting those with compromised kidney function.

Bifidobacterium appears to possess metabolic pathways that may help reduce uremic toxin levels. Bifidobacterium, including species B. longum, B. adolescentis, B. animalis, and B. dentium, possess a variety of aromatic ring metabolism pathways that converge on benzoyl-CoA, with subsequent ring cleavage via beta oxidation (Kanehisa and Goto. 2000. Nucleic Acids Res) p-cresol, the gut microbe-derived precursor of p- cresol sulfate, can be actively metabolized by Bifidobacterium via such a pathway, supporting a mechanistic link between increasing Bifidobacterium levels and decreasing p-cresol sulfate levels. Bifidobacterium species B. longum and B. dentium possess the ability to convert indoxyl, the microbe-derived precursor of indoxyl sulfate, into N-acetylisatin via acetylindoxyl oxidase (Figure 4B; Kanehisa and Goto. 2000. Nucleic Acids Res). Therefore, RS-dependant increases in Bifidobacterium may reduce serum indoxyl sulfate levels by enhancing metabolism of indoxyl into other metabolites in the gut. Alternatively, Bifidobacterium may alter the lumen pH of the intestines by creating lactic acid during RS degradation. Bifidobacterium-dependent reductions in pH have recently been shown to inhibit the activity of tryptophanase in a variety of commensal bacteria, thereby reducing the production of 5-hydroxyindole from 5-hydroxytryptophan (Waclawikova et al. 2021. PLoS Biol). Tryptophanase also converts tryptophan to indole, the precursor of indoxyl sulfate. Therefore, the RS-dependent increases in Bifidobacterium documented here could reduce serum indoxyl sulfate levels by lowering the pH of the intestinal lumen and reducing tryptophanase-dependent conversion of tryptophan to indole, limiting the conversion of indole to indoxyl sulfate. It is possible that tyrosinase is similarly pH sensitive, and that RS-dependent increases in Bifidobacterium drive reductions in intestinal pH, thereby inhibiting p-cresol sulfate production from tyrosine. Such a mechanisms would explain how increasing Bifidobacterium with RS supplementation can drive down the abundance of indoxyl sulfate and p-cresol sulfate in serum.

Overall, RS consumption led to significant increases in Bifidobacterium and significant decreases in indoxyl sulfate and isobutyrate on average, but had no effect on Blautia, Dorea, p-cresol sulfate, or isovalerate levels. However, we detected discrete correlations between changes in some of these bacteria and metabolites, suggesting that a person’s gut metabolomic response to RS consumption is dependent upon, and can be predicted by, changes in the microbiome. Surprisingly, our data suggest that beneficial metabolite reductions are dependent upon the discrete bacteria in the microbiome and do not simply represent a protein-to-carbohydrate shift in fermentation. Future studies examining complementary approaches to support microbiome-dependent metabolic changes, such as RS supplementation coupled with probiotic Bifidobacterium administration, may help refine how the use of the correlations identified in this study can be better used to guide therapies for people seeking to reduce protein fermentation metabolite levels.

Thus, as discussed above, gut microbiome dysbiosis contributes to the toxic burden on kidneys. Surprisingly, changes in the levels of Bifidobacterium, Blautia, and/or Dorea serve as markers for changes in the microbiome-mediate burden placed on the kidneys. Specifically, it is believed that levels of Bifidobacterium, Blautia, and/or Dorea levels serve as a marker of kidney disease-related parameters but it is unclear to what extent each genus is a driver of microbiome-mediated kidney burden or relief. Accordingly, monitoring levels of Bifidobacterium, Blautia, and/or Dorea in combination with at least one kidney disease-related parameter provides information on the effectiveness of gut microbiome related treatments. If Blautia and/or Dorea levels decrease in combination with improvements in one or more of the kidney disease-related parameters, this indicates that the individual can be treated using gut microbiome-based treatments. Similarly, if Bifidobacterium levels increase in combination with improvements in one or more of the kidney disease-related parameters, this also indicates that the individual can be treated using gut microbiome.

Alternatively, if Blautia and/or Dorea levels decrease but the kidney disease- related parameters do not improve, or if Bifidobacterium levels increase and the kidney disease-related parameters do not improve, the progression of kidney disease may be more heavily influenced by other factors, for example, genetic predisposition, diet, activity levels or the like and the gut microbiome modulating treatment should be stopped and replaced with more conventional treatments for kidney disease.

In summary, screening for Bifidobacterium, Blautia and/or Dorea levels in combination with kidney disease-related parameters will identify those individuals who will benefit from positive modulation of the gut microbiome. The effectiveness of this strategy can then be measured by monitoring Bifidobacterium, Blautia and/or Dorea levels in combination with kidney disease-related measures. Our findings support these statements for the following reasons: 1 ) Changes in Bifidobacterium and changes in both indoxyl sulfate and p-cresol sulfate were negatively correlated in those consuming the prebiotic supplement but not the placebo, 2) Changes in both Blautia and Dorea and changes in both isobutyrate and isovalerate were positively correlated in those consuming the prebiotic supplement but not the placebo, and 3) the discrete correlations between metabolites and genera does not support a protein-to-carbohydrate fermentation transition, thereby necessitating microbiome monitoring the judge the efficacy to microbiome interventions that target these genera.

This screen holds several advantages over methods focused on modifying the gut microbiota as a means of improving kidney disease-related measures. First, our data disprove the concept that shifting the microbiome fermentation dependence from protein- to-carbohydrate is generally beneficial. Rather, our data suggest that different microbiota are responsible for modulating various aspects of protein-derived metabolite formation in the context of enhanced carbohydrate fermentation. Some individuals might benefit most from uremic toxin (indoxyl sulfate and p-cresol sulfate) reduction and would need to focus on microbiome interventions that increase Bifidobacterium levels. Other individuals might benefit most from ammonia and urea reduction, toxins that are associated with BCFA (isobutyrate and isovalerate) production, and need to focus on microbiome interventions that decrease Blautia and/or Dorea levels. Such personalization was previously unappreciated in making kidney disease-related recommendations.

Second, we have been able to frame these discoveries in the context of known bacterial metabolic pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG; Kanehisa and Goto. 2000. Nucleic Acids Res) and Bifidobacterium-assoc\ate0 changes in pH (Waclawikova et al. 2021. PLoS Biol). So unlike previous correlation studies linking changes in bacterial abundance to changes in health parameters where the relationship between the bacterium and the health parameter were unknown (Bush and Alfa. 2018. JARCP), we can more clearly predict the outcome of the intervention while monitoring the microbiome. Monitoring these microbiome biomarkers could indicate that combinations of interventions might achieve a better kidney disease-related parameter outcome. For example, those with chronic kidney disease needing to dramatically reduce indoxyl sulfate levels might find that dietary restriction of tryptophan, the dietary precursor of indoxyl sulfate, is beneficial in combination with probiotic Bifidobacterium administration with prebiotic resistant starch supplementation because lower dietary tryptophan levels in conjunction with increased abundance and activity of Bifidobacterium may more efficiently direct the gut microbiome towards alternatives to indoxyl metabolites. Conversely, individuals with hepatic encephalopathy for whom ammonia is inefficiently detoxified and removed may need to restrict all dietary protein in combination with resistant starch supplementation and phages targeting Blautia and Dorea to hinder the gut microbiome’s ability to generate ammonia via Stickland fermentation.

Finally, the use of Pearson correlation coefficients, which determine linear proportionality, is particularly helpful because it allows the proportional improvement in kidney disease-related parameters to be inferred from the changes in abundance of genera in the gut microbiome. These results suggest that as long as a genus is detectable (ie. A non-zero value), changes in the abundance of that genus will be informative for the health outcome of the host. Practically speaking, this means that the screen is predictive regardless of absolute levels, be they minimum or maximum values. This provides a generic method by which to test the efficacy of microbiome-based therapies for improving kidney disease-related metabolites, and, by extension, improving the health of those individuals for whom kidney function may be adversely affected.

MATERIALS AND METHODS Investigational product

The resistant starch (RS) used in this study was MSPrebiotic (MSPrebiotics Inc., Carberry, MB), an unmodified resistant potato starch (RS type 2) with an RS content of 60% (AOAC 2002.02). MSPrebiotic has been previously described (Alfa et al. 2018. Front Med, Alfa et al. 2018. Clin Nutr). The placebo used was fully digestible corn starch (Amioca; Ingredion, Brampton, ON) and contains no RS. The products were packaged in identical foil sachets distinguished by stickers with participant codes (Source Nutraceutical, Winnipeg, MB).

Clinical trial structure, per protocol determination, and sample collection This was a prospective, randomized, double-blinded, placebo-controlled study conducted in Winnipeg, MB, Canada by Source Nutraceutical and Cliantha Research (formerly Hill Top Research). A formal power analysis was not conducted as the study was designed to replicate a previous clinical trial (Alfa et al. 2018. Front Med, Alfa et al. 2018. Clin Nutr). Research and ethics approval was obtained from Advarra IRB (Aurora, ON) prior to implementation. The study protocol was authorized by Health Canada (Submission #229186; “Notice of Authorization” dated May 23, 2017) and listed on the NIH ClinicalTrials.gov website (Identifier: NCT03910153). All protocol modifications were reported to Health Canada and Advarra IRB for approval prior to implementation of changes. Participants were segregated by age into sub-groups (30-50 years; MID or >70 years; ELD) and then assigned to placebo or study product based on the randomization list generated by Karmic Life Sciences (Mumbai, Maharashtra, India). Trial participants, clinical investigators, outcome assessors, and data analysts were blinded to which treatment participants were assigned. All information collected for the purpose of the study was kept in a locked and secured area. All information collected and sent for statistical analyses only had a study number and no participant identifiers. All participant identifiers were treated in confidence and in accordance with the Personal Health Information Act of Manitoba.

The study was explained verbally and in written format to eligible participants, and all participants provided written informed consent. Participants were informed that they could request to withdraw from the clinical study at any time without adverse affects. To reduce the possibility that any microbiome changes were due to confounding factors unrelated to consumption of resistant starch (RS), there were a number of exclusion criteria including: pregnancy, planned pregnancy, or breastfeeding during the study, Crohn's disease or other inflammatory bowel disease, individuals with systemic lupus erythematosus or on cancer chemotherapy, pre-diabetes or diabetes, thyroid disease, renal disease, hepatic disease, previous gastrointestinal surgery (intestinal resection, gastric bypass, colorectal surgery), individuals consuming probiotics (including potential probiotic foods like yogurt), individuals on antibiotics at time of recruitment or on antibiotics within the previous five weeks, individuals experiencing dysphagia, subjects using additional fiber supplements, subjects allergic to potato or corn, and individuals on digestants, emetics, anti-emetics, medications for acid peptic disease or taking antacids. Female participants took pregnancy tests to ensure they were not pregnant during the study. There were no changes to the normal daily diet consumed by participants other than the requirement that they did not consume probiotic-containing products.

We arrived on the per protocol population after excluding participants who consumed probiotic food (n = 1 ), took oral antibiotics (n = 4), those not providing fasting samples (n = 6), and those for whom 16s rRNA read counts were insufficient (n = 6). Additionally, enrollment and/or baseline data from participants who did not complete the trial were not analyzed, thereby facilitating paired sample analysis. In total, data was analyzed for 81 participants, 44 of whom received RS (20 MID, 24 ELD; MSPrebiotic®) and 37 of whom received placebo (15 MID, 22 ELD; Amioca corn starch). Participants consumed 30g/day placebo for a two-week run-in period, before either continuing with 30g/day placebo or consuming 30g/day RS for 12 weeks. Subjects were advised to mix the product in to 250 ml_ of non-heated fluid or non-heated semi-solid food, and those taking medication were advised to take the product either 2 hours before or after taking the medication. Samples were collected at enrollment (fasting blood glucose), baseline (fasting serum and stool), and at the end of the trial (fasting blood glucose, serums and stool). Blood glucose and serum samples were collected in the laboratory, while stool samples were collected in an OMNIgene-Gut kits (DNA Genotek, Ottawa, ON), which stabilizes the microbiome DNA. Blood glucose was analyzed at (Diagnostic Services Manitoba, Winnipeg, MB), while serum samples for indoxyl sulfate and p-cresol sulfate analysis were stored at -80°C (MRM Proteomics, Montreal, QC) before mass spectroscopy analysis (The Metabolomics Innovation Centre/The UVic-Genome BC Proteomics Centre, Victoria, BC). BCFA levels were measured by gas chromatography and microbiome sequencing was directed at the 16s rRNA V4 region (Microbiome Insights, Vancouver, BC).

Mass Spectroscopy

UPLC-MRM/MS was carried out on an Agilent 1290 UHPLC (Agilent Technologies, Santa Clara, CA) coupled to a Sciex 4000 OTRAP mass spectrometer (AB Sciex, Framingham, MA) operated in the multiple-reaction monitoring mode with negative-ion detection. An internal standard solution containing lndoxyl-3-sulfate-D3 and p-cresol- sulfate-D4 was prepared in MeOFI-acetonitrile and serially diluted with water. Serum samples were thawed at room temperature, aliquots mixed with the internal standard solution, vortexed, and sonicated in a water bath, before centrifugal clarification. Aliquots of supernatant were mixed with water to create sample solutions. A pooled sample of 10 randomly selected aliquots was also prepared as the OC sample solution, which was injected after every 20 samples. UPLC-MRM/MS data were acquired with Sciex Analyst software and batch processed with Sciex MultiOuant software (AB Sciex). Linear calibration curves of indoxyl sulfate and p-cresol sulfate were constructed using standard solution concentrations for each analyte with an appropriate concentration range versus analyte-to-internal standard peak area ratios. Concentrations of indoxyl sulfate and p- cresol sulfate detected in each sample were calculated by interpolating the calibration curves with the analyte-to-internal standard peak area ratios measured from each sample. Liquid chromatography

The short chain fatty acid (SCFA) extraction procedure and analysis, which includes BCFAs, is similar to that of Zhao et al (2006). Briefly, aliquots of fecal samples collected in OMNI-Gut kits (DNA Genotek) were resuspended in distilled water, homogenized using MP Bio FastPrep (MP Biomedicals, Irvine, CA), and acidified with HCI to a final pH of 2.0. Suspensions were incubated and centrifuged to separate the supernatant. Fecal supernatants were then spiked with 2-Ethylbutyric acid for a final concentration of 1 mM. SCFA were detected using gas chromatography (Thermo Trace 1310 with TG-WAXMS A GC Column, 30 m, 0.32 mm, 0.25 urn), coupled to a flame ionization detector (Thermo Fisher Scientific, Waltham, MA).

Microbiome analysis

16Sv4 amplicons generated from fecal samples collected in OMNIgene-Gut kits (DNA Genotek) were sequenced on a MiSeq platform (lllumina, San Diego, CA). MiSeq- generated Fastq files were quality-filtered and clustered into 97% similarity operational taxonomic units (OTUs) using the mothur software package [http://www.mothur.org]. The resulting dataset had 153660 OTUs (including those occurring once with a count of 1 , or singletons). An average of 33403 quality-filtered reads were generated per sample. Sequencing quality for R1 and R2 was determined using FastQC 0.11.5. Reads identified to the genus level are indicated as the relative abundance.

Statistical analysis

Differences between RS and placebo treatments were determined using a two- tailed, paired sample t-test (Excel, Microsoft, Redmond, WA). Changes in abundance were determined by subtracting baseline values from week 14 values. Pearson coefficients representing correlations between the change in genus abundance and change in metabolite were determined, with the magnitude of metabolite change being proportional to the magnitude of change in bacterial abundance (Excel). Values are considered statistically significant when p < 0.05. To correct for multiple testing, the Benjamini-Hochberg method was used at a false discovery rate of 0.1 (Benjamini and Hochberg. 1995. J R Stat Soc B).

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Table 1 . Blood glucose levels and the effect of RS. Mean blood glucose values (mmol/L) +/- SD are indicated for each treatment in a given age group at each timepoint. Two-tailed, paired t-tests were used to determine statistical significance.

Table 2: Indoxyl sulfate-genus Pearson correlation coefficients (ή for all RS consuming participants ordered by p value and compared to critical Benjamini-Hochberg critical values.

Rank Genus Pearson (r) All p Value All Critical Value

1 Bifidobacterium 0.463354951 0.001538 0.002222222

2 Turicibacter 0.419361113 0.004603 0.004444444

3 Butyricimonas 0.360655138 0.016174 0.006666667

4 Haemophilus 0.329549147 0.028932 0.008888889

5 Prevotella 0.317087218 0.035978 0.011111111

6 Akkermansia 0.225065334 0.141991 0.013333333

7 Adlercreutzia 0.20587683 0.18191 0.015555556

8 Bacteroides 0.202749031 0.188528 0.017777778

9 Escherichia 0.199843354 0.193489 0.02

10 Acidaminococcus 0.180931023 0.23994 0.022222222 11 Lactobacillus 0.168506434 0.27568 0.024444444 12 Veillonella 0.166244821 0.281519 0.026666667

13 Parabacteroides 0.162998003 0.290728 0.028888889

14 Catenibactehum 0.15991074 0.302588 0.031111111

15 Collinsella 0.155955511 0.312232 0.033333333

16 Diaiister 0.145552165 0.345998 0.035555556

17 Paraprevotella 0.133300681 0.388349 0.037777778

18 Faecalibactehum 0.124032548 0.422584 0.04

19 Lachnobactehum 0.118500168 0.445546 0.042222222

20 Phascolarctobactehum 0.117388635 0.448269 0.044444444 21 Subdoligranulum 0.114230688 0.460436 0.046666667 22 Desuifovibrio 0.111377747 0.47318 0.048888889

23 Odoribacter 0.110039393 0.4772 0.051111111

24 Roseburia 0.104524398 0.501696 0.053333333

25 Oxalobacter 0.102408908 0.510002 0.055555556

26 Bilophila 0.095887622 0.539607 0.057777778

27 Blautia 0.094500912 0.541753 0.06

28 Succinivibrio 0.072661201 0.639549 0.062222222

29 Aiistipes 0.069903903 0.656284 0.064444444

30 Holdemania 0.067179385 0.665655 0.066666667

31 Butyricicoccus 0.061425693 0.692156 0.068888889

32 Megasphaera 0.055105682 0.72241 0.071111111

33 Ruminococcus 0.052637035 0.734535 0.073333333

34 Eggerthella 0.049971299 0.747702 0.075555556

35 Eubactehum 0.035630553 0.821552 0.077777778

36 Coprococcus 0.034964654 0.826571 0.08

37 Streptococcus 0.031246893 0.840661 0.082222222

38 Lachnospira 0.031540666 0.841669 0.084444444

39 Clostridium 0.029114904 0.851766 0.086666667

40 Anaerostipes 0.028166837 0.856317 0.088888889

41 Oscillospira 0.019648591 0.89951 0.091111111

42 SMB53 0.016643179 0.917897 0.093333333

43 Sutterella 0.013460449 0.933253 0.095555556

44 Anaerotruncus 0.004026033 0.979441 0.097777778

45 Dorea 0.001304406 0.99486 0.1 Table 3. Isobutyrate-genus Pearson correlation coefficients (ή for all RS consuming participants ordered by p value and compared to critical Benjamini-Hochberg critical values.

Rank Genus Pearson p value Critical Value

1 Dorea 0.57423 5E-05 0.002222222

2 Blautia 0.56556 6E-05 0.004444444

3 Eggerthella 0.38503 0.0099 0.006666667

4 Butyricicoccus 0.37922 0.0111 0.008888889

5 Collinsella 0.34265 0.0228 0.011111111

6 Anaerostipes 0.2914 0.0553 0.013333333

7 SMB53 0.28078 0.0649 0.015555556

8 Oxalobacter 0.24172 0.114 0.017777778

9 Coprococcus 0.22496 0.1422 0.02

10 Alistipes 0.2231 0.1457 0.022222222 11 Desulfovibrio 0.2145 0.1631 0.024444444 12 Roseburia 0.2111 0.1692 0.026666667

13 Bacteroides 0.2109 0.1713 0.028888889

14 Phascolarctobacterium 0.1974 0.1999 0.031111111

15 Lachnobacterium 0.1869 0.2267 0.033333333

16 Dialister 0.1846 0.2318 0.035555556

17 Parabacteroides 0.17668 0.2515 0.037777778

18 Faecalibacteria 0.1672 0.2786 0.04

19 Prevotella 0.16495 0.2848 0.042222222

20 Holdemania 0.1469 0.3443 0.044444444 21 Lactobacillus 0.14138 0.3602 0.046666667 22 Butyricimonas 0.1382 0.3717 0.048888889

23 Ruminococcus 0.13511 0.3819 0.051111111

24 Akkermansia 0.134 0.3894 0.053333333

25 Megasphaera 0.1331 0.3894 0.055555556

26 Turicibacter 0.1318 0.3967 0.057777778

27 Subdoligranulum 0.11622 0.4526 0.06

28 Clostridium 0.10931 0.48 0.062222222

29 Bifidobacterium 0.10905 0.4812 0.064444444

30 Sutterella 0.1033 0.5058 0.066666667

31 Lachnospira 0.0962 0.5353 0.068888889

32 Anaerotruncus 0.0908 0.5612 0.071111111

33 Catenibacterium 0.09 0.5656 0.073333333

34 Adlercreutzia 0.084 0.5922 0.075555556

35 Escherichia 0.07325 0.6368 0.077777778

36 Odoribacter 0.06777 0.6624 0.08

37 Streptococcus 0.06662 0.6675 0.082222222

38 Acidaminococcus 0.0631 0.6845 0.084444444

39 Oscillospira 0.05987 0.6998 0.086666667

40 Paraprevotella 0.05187 0.7384 0.088888889

41 Haemophilus 0.0192 0.9021 0.091111111

42 Eubacterium 0.0131 0.9333 0.093333333

43 Veillonella 0.00325 0.9836 0.095555556

44 Succinivibrio 0.0036 0.9846 0.097777778

45 Bilophila 0.0025 0.9897 0.1 Table 4. Pearson coefficients (ή and p values for BCFA-Genus correlations by intervention.

Claims

1. A method for determining efficacy of a microbiome modulating treatment for harmful kidney disease-related metabolites in an individual at risk of developing kidney disease or who has developed kidney disease or who has kidney disease, said method comprising: detecting Bifidobacterium levels in a first gut microbiome sample from the individual at a first time point; determining a first measurement of a kidney disease-related parameter of the individual at the first time point; administering to the individual an effective amount of a gut microbiome modulating treatment on a dosage regimen or schedule for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Bifidobacterium levels in the second sample; determining a second measurement of the kidney disease-related parameter of the individual at the second time point; comparing Bifidobacterium levels in the second gut microbiome sample to Bifidobacterium levels in the first gut microbiome sample, and comparing the first measurement of the kidney disease-related parameter and the second measurement of the kidney disease-related parameter, wherein if the Bifidobacterium levels in the second sample are higher than Bifidobacterium levels in the first sample and the second kidney disease-related parameter is improved compared to the first kidney disease-related parameter, continuing the dosage regimen for the individual.

2. The method according to claim 1 wherein the individual who is at risk of developing kidney disease is at risk based on genetic predisposition, familial history, heredity, lifestyle or one or more kidney disease-related parameters being abnormal.

3. The method according to claim 1 wherein the gut microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; and galactomannan polysaccharides.

4. The method according to claim 3 wherein the probiotic genera is selected from the group consisting of: Bifidobacterium; Staphylococcus; Clostridium; Lactobacilli; Prevotella; Barnsiella; Parasutterella; and combinations thereof.

5. The method according to claim 3 wherein the resistant starch is RS1 , RS2, RS3, RS4, or RS5.

6. The method according to claim 1 wherein the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that increase the availability of resistant starch and/or prebiotics and/or other fermentation substrates to Bifidobacterium in the digestive tract; and antibiotics that target bacteria that inhibit the growth of Bifidobacterium.

7. The method according to claim 1 wherein the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that increase the availability of resistant starch and/or prebiotics and/or other fermentation substrates to Bifidobacterium in the digestive tract; antibiotics that target bacteria that inhibit the growth of Bifidobacterium ; mixed plant cell wall fibers; beta-glucans; resistant dextrins; resistant maltodextrins; limit dextrins; polydextrose; alginate; pectin polysaccharides; hydroxypropylmethylcellulose; chitin; chondroitin-containing compounds; and glucosamine-containing compounds.

8. The method according to claim 7 wherein the mixed plant cell wall fibers comprise two or more of the following plant cell wall fibers in varying proportions: cellulose, pectin, lignin, beta-glucan, and arabinoxylan regardless of source.

9. The method according to claim 1 wherein the kidney disease-related parameter is selected from the group consisting of: serum and/or blood indoxyl sulfate levels; stool indoxyl levels; serum and/or blood p-cresol sulfate levels; stool p-cresol levels; stool isobutyrate levels; stool isovalerate levels; serum and/or blood ammonia levels; serum and/or blood urea levels; urea levels in urine; Kt/V values; urea reduction ratios; serum and/or blood urea nitrogen levels; serum and/or blood creatinine levels; creatinine levels in urine; serum and/or blood albumin levels; serum and/or blood protein levels; protein levels in urine; estimated glomerular filtration rates.

10. The method according to claim 1 wherein the suitable period of time is from 1 week to 6 months.

11. The method according to claim 1 wherein Bifidobacterium levels are measured by using a method selected from the group consisting of: real-time polymerase chain reaction (RT-PCR)-based methods; qualitative PCR (qPCR) based methods; microbiome sequencing; shotgun metagenomic sequencing; quantitative fluorescent in situ hybridization (FISH); antibody-based methods; and cell-binding based methods.

12. The method according to claim 1 wherein the gut microbiome modulating compound is resistant potato starch.

13. The method according to claim 1 wherein the effective amount is 2 to 40 g per day of resistant potato starch.

14. The method according to claim 13 wherein the effective amount is administered in one or more doses during the day.

15. A method for determining efficacy of a microbiome modulating treatment for harmful kidney disease-related metabolites in an individual at risk of developing kidney disease or who has developed kidney disease or who has kidney disease, said method comprising: detecting Bifidobacterium levels in a first gut microbiome sample from the individual at a first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Bifidobacterium levels in the second sample; and comparing Bifidobacterium levels in the second gut microbiome sample to Bifidobacterium levels in the first gut microbiome sample, wherein if the Bifidobacterium levels in the second sample are higher than Bifidobacterium levels in the first sample, continuing the dosage regimen for the individual.

16. The method according to claim 15 wherein at the first time point and the second time point, at least one kidney disease-related parameter of the individual is measured and these two parameters are also compared.

17. The method according to claim 15 wherein the individual who is at risk of developing kidney disease is at risk based on genetic predisposition, familial history, heredity, lifestyle or one or more kidney disease-related parameters being abnormal.

18. The method according to claim 15 wherein the gut microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; and galactomannan polysaccharides.

19. The method according to claim 18 wherein the probiotic genera is selected from the group consisting of: Bifidobacterium; Staphylococcus; Clostridium; Lactobacilli; Prevotella; Barnsiella; Parasutterella; and combinations thereof.

20. The method according to claim 18 wherein the resistant starch is RS1 , RS2, RS3, RS4, or RS5.

21. The method according to claim 15 wherein the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that increase the availability of resistant starch and/or prebiotics and/or other fermentation substrates to Bifidobacterium in the digestive tract; and antibiotics that target bacteria that inhibit the growth of Bifidobacterium.

22. The method according to claim 15 wherein the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that increase the availability of resistant starch and/or prebiotics and/or other fermentation substrates to Bifidobacterium in the digestive tract; and antibiotics that target bacteria that inhibit the growth of Bifidobacterium ; mixed plant cell wall fibers; beta-glucans; resistant dextrins; resistant maltodextrins; limit dextrins; polydextrose; alginate; pectin polysaccharides; hydroxypropylmethylcellulose; chitin; chondroitin-containing compounds; and glucosamine-containing compounds.

23. The method according to claim 22 wherein the mixed plant cell wall fibers comprise two or more of the following plant cell wall fibers in varying proportions: cellulose, pectin, lignin, beta-glucan, and arabinoxylan regardless of source.

24. The method according to claim 16 wherein the kidney disease related parameter is selected from the group consisting of: serum and/or blood indoxyl sulfate levels; stool indoxyl levels; serum and/or blood p-cresol sulfate levels; stool p-cresol levels; stool isobutyrate levels; stool isovalerate levels; serum and/or blood ammonia levels; serum and/or blood urea levels; urea levels in urine; Kt/V values; urea reduction ratios; serum and/or blood urea nitrogen levels; serum and/or blood creatinine levels; creatinine levels in urine; serum and/or blood albumin levels; serum and/or blood protein levels; protein levels in urine; estimated glomerular filtration rates.

25. The method according to claim 15 wherein the suitable period of time is from 1 week to 6 months.

26. The method according to claim 15 wherein Bifidobacterium levels are measured by using a method selected from the group consisting of: real-time polymerase chain reaction (RT-PCR)-based methods; qualitative PCR (qPCR) based methods; microbiome sequencing; shotgun metagenomic sequencing; quantitative fluorescent in situ hybridization (FISH); antibody-based methods; and cell-binding based methods.

27. The method according to claim 15 wherein the gut microbiome modulating compound is resistant potato starch.

28. The method according to claim 15 wherein the effective amount is 2 to 40 gram per day of resistant potato starch.

29. The method according to claim 28 wherein the effective amount is administered in one or more doses during the day.

30. A method for determining efficacy of a microbiome modulating treatment for harmful kidney disease-related metabolites in an individual being administered said microbiome modulating treatment, said method comprising: detecting Bifidobacterium levels in a first gut microbiome sample from the individual at a first time point; following a suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Bifidobacterium levels in the second sample; and comparing Bifidobacterium levels in the second gut microbiome sample to Bifidobacterium levels in the first gut microbiome sample, wherein if the Bifidobacterium levels in the second sample are higher than Bifidobacterium levels in the first sample, the microbiome modulating treatment is effective.

31. A method for determining efficacy of a gut microbiome modulating treatment for harmful kidney disease-related metabolites in an individual being administered said microbiome modulating treatment, said method comprising: detecting Bifidobacterium levels in a first gut microbiome sample from the individual at a first time point; determining a first measurement of a kidney disease-related parameter of the individual at the first time point; following a suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Bifidobacterium levels in the second sample; determining a second measurement of the kidney disease-related parameter of the individual at the second time point; comparing Bifidobacterium levels in the second gut microbiome sample to Bifidobacterium levels in the first gut microbiome sample, and comparing the first measurement of the kidney disease-related parameter and the second measurement of the kidney disease-related parameter, wherein if the Bifidobacterium levels in the second sample are higher than Bifidobacterium levels in the first sample and the second measurement of the kidney disease-related parameter is improved compared to the first measurement of the kidney disease-related parameter, the gut microbiome modulating treatment is effective.

32. A method for determining efficacy of a microbiome modulating treatment for harmful kidney disease-related metabolites in an individual at risk of developing kidney disease or who has developed kidney disease or who has kidney disease, said method comprising: detecting Blautia and/or Dorea levels in a first gut microbiome sample from the individual at a first time point; determining a first measurement of a kidney disease-related parameter of the individual at the first time point; administering to the individual an effective amount of a gut microbiome modulating treatment on a dosage regimen or schedule for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Blautia and/or Dorea levels in the second sample; determining a second measurement of the kidney disease-related parameter of the individual at the second time point; comparing Blautia levels in the second gut microbiome sample to Blautia levels in the first gut microbiome sample, and/or comparing Dorea levels in the second gut microbiome sample to Dorea levels in the first gut microbiome sample, and comparing the first measurement of the kidney disease-related parameter and the second measurement of the kidney disease-related parameter, wherein if the Blautia and/or Dorea levels in the second sample are lower than Blautia and/or Dorea levels in the first sample and the second kidney disease-related parameter is improved compared to the first kidney disease-related parameter, continuing the dosage regimen for the individual.

33. The method according to claim 32 wherein the individual who is at risk of developing kidney disease is at risk based on genetic predisposition, familial history, heredity, lifestyle or one or more kidney disease-related parameters being abnormal.

34. The method according to claim 32 wherein the gut microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; and galactomannan polysaccharides.

35. The method according to claim 34 wherein the probiotic genera is selected from the group consisting of: Bifidobacterium; Staphylococcus; Clostridium; Lactobacilli; Prevotella; Barnsiella; Parasutterella; and combinations thereof.

36. The method according to claim 34 wherein the resistant starch is RS1 , RS2, RS3, RS4, or RS5.

37. The method according to claim 32 wherein the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that decrease the availability of protein and/or peptides and/or amino acids and/or other fermentation substrates to Blautia and/or Dorea in the digestive tract; and antibiotics that target Blautia and/or Dorea and andy other bacteria that promote the growth of Blautia and/or Dorea.

38. The method according to claim 32 wherein the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that increase the availability of protein and/or peptides and/or amino acids and/or other fermentation substrates to Blautia and/or Dorea in the digestive tract; and antibiotics that target Blautia and/or Dorea and andy other bacteria that promote the growth of Blautia and/or Dorea ; mixed plant cell wall fibers; beta-glucans; resistant dextrins; resistant maltodextrins; limit dextrins; polydextrose; alginate; pectin polysaccharides; hydroxypropylmethylcellulose; chitin; chondroitin- containing compounds; and glucosamine-containing compounds.

39. The method according to claim 38 wherein the mixed plant cell wall fibers comprise two or more of the following plant cell wall fibers in varying proportions: cellulose, pectin, lignin, beta-glucan, and arabinoxylan regardless of source.

40. The method according to claim 32 wherein the kidney disease-related parameter is selected from the group consisting of: serum and/or blood indoxyl sulfate levels; stool indoxyl levels; serum and/or blood p-cresol sulfate levels; stool p-cresol levels; stool isobutyrate levels; stool isovalerate levels; serum and/or blood ammonia levels; serum and/or blood urea levels; urea levels in urine; Kt/V values; urea reduction ratios; serum and/or blood urea nitrogen levels; serum and/or blood creatinine levels; creatinine levels in urine; serum and/or blood albumin levels; serum and/or blood protein levels; protein levels in urine; estimated glomerular filtration rates.

41. The method according to claim 32 wherein the suitable period of time is from 1 week to 6 months.

42. The method according to claim 32 wherein Blautia and/or Dorea levels are measured by using a method selected from the group consisting of: real-time polymerase chain reaction (RT-PCR)-based methods; qualitative PCR (qPCR) based methods; microbiome sequencing; shotgun metagenomic sequencing; quantitative fluorescent in situ hybridization (FISH); antibody-based methods; and cell-binding based methods.

43. The method according to claim 32 wherein the gut microbiome modulating compound is resistant potato starch.

44. The method according to claim 32 wherein the effective amount is 2 to 40 g per day of resistant potato starch.

45. The method according to claim 44 wherein the effective amount is administered in one or more doses during the day.

46. A method for determining efficacy of a microbiome modulating treatment for harmful kidney disease-related metabolites in an individual at risk of developing kidney disease or who has developed kidney disease or who has kidney disease, said method comprising: detecting Blautia and/or Dorea levels in a first gut microbiome sample from the individual at a first time point; administering to the individual a microbiome modulating treatment on a dosage regimen for a suitable period of time; following the suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Blautia and/or Dorea levels in the second sample; and comparing Blautia and/or Dorea levels in the second gut microbiome sample to Blautia and/or Dorea levels in the first gut microbiome sample, wherein if the Blautia levels in the second sample are lower than Blautia levels in the first sample and/or Dorea levels in the second sample are lower than Dorea levels in the first sample, continuing the dosage regimen for the individual.

47. The method according to claim 46 wherein at the first time point and the second time point, at least one kidney disease-related parameter of the individual is measured and these two parameters are also compared.

48. The method according to claim 46 wherein the individual who is at risk of developing kidney disease is at risk based on genetic predisposition, familial history, heredity, lifestyle or one or more kidney disease-related parameters being abnormal.

49. The method according to claim 46 wherein the gut microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; and galactomannan polysaccharides.

50. The method according to claim 49 wherein the probiotic genera is selected from the group consisting of: Bifidobacterium; Staphylococcus; Clostridium; Lactobacilli; Prevotella; Barnsiella; Parasutterella; and combinations thereof.

51. The method according to claim 49 wherein the resistant starch is RS1 , RS2, RS3, RS4, or RS5.

52. The method according to claim 46 wherein the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that increase the availability of proteins and/or peptides and/or amino acids and/or other fermentation substrates to Blautia and/or Dorea in the digestive tract; and antibiotics that target Blautia and/or Dorea and/or any other bacteria that promote the growth of Blautia and/or Dorea.

53. The method according to claim 46 wherein the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that increase the availability of proteins and/or peptides and/or amino acids and/or other fermentation substrates to Blautia and/or Dorea in the digestive tract; and antibiotics that target Blautia and/or Dorea and/or any other bacteria that promote the growth of Blautia and/or Dorea ; mixed plant cell wall fibers; beta-glucans; resistant dextrins; resistant maltodextrins; limit dextrins; polydextrose; alginate; pectin polysaccharides; hydroxypropylmethylcellulose; chitin; chondroitin- containing compounds; and glucosamine-containing compounds.

54. The method according to claim 53 wherein the mixed plant cell wall fibers comprise two or more of the following plant cell wall fibers in varying proportions: cellulose, pectin, lignin, beta-glucan, and arabinoxylan regardless of source.

55. The method according to claim 46 wherein the kidney disease related parameter is selected from the group consisting of: serum and/or blood indoxyl sulfate levels; stool indoxyl levels; serum and/or blood p-cresol sulfate levels; stool p-cresol levels; stool isobutyrate levels; stool isovalerate levels; serum and/or blood ammonia levels; serum and/or blood urea levels; urea levels in urine; Kt/V values; urea reduction ratios; serum and/or blood urea nitrogen levels; serum and/or blood creatinine levels; creatinine levels in urine; serum and/or blood albumin levels; serum and/or blood protein levels; protein levels in urine; estimated glomerular filtration rates.

56. The method according to claim 46 wherein the suitable period of time is from 1 week to 6 months.

57. The method according to claim 46 wherein Blautia and/or Dorea levels are measured by using a method selected from the group consisting of: real-time polymerase chain reaction (RT-PCR)-based methods; qualitative PCR (qPCR) based methods; microbiome sequencing; shotgun metagenomic sequencing; quantitative fluorescent in situ hybridization (FISH); antibody-based methods; and cell-binding based methods.

58. The method according to claim 46 wherein the gut microbiome modulating compound is resistant potato starch.

59. The method according to claim 46 wherein the effective amount is 2 to 40 g per day of resistant potato starch.

60. The method according to claim 59 wherein the effective amount is administered in one or more doses during the day.

61. A method for determining efficacy of a microbiome modulating treatment for harmful kidney disease-related metabolites in an individual being administered said microbiome modulating treatment, said method comprising: detecting Blautia and/or Dorea levels in a first gut microbiome sample from the individual at a first time point; following a suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Blautia and/or Dorea levels in the second sample; and comparing Blautia and/or Dorea levels in the second gut microbiome sample to Blautia and/or Dorea levels in the first gut microbiome sample, wherein if the Blautia levels in the second sample are higher than Blautia levels in the first sample, and/or Dorea levels in the second sample are higher than Dorea levels in the first sample, the microbiome modulating treatment is effective.

62. A method for determining efficacy of a gut microbiome modulating treatment for harmful kidney disease-related metabolites in an individual being administered said microbiome modulating treatment, said method comprising: detecting Blautia and/or Dorea levels in a first gut microbiome sample from the individual at a first time point; determining a first measurement of a kidney disease-related parameter of the individual at the first time point; following a suitable period of time, obtaining a second gut microbiome sample from the individual; detecting Blautia and/or Dorea levels in the second sample; determining a second measurement of the kidney disease-related parameter of the individual at the second time point; comparing Blautia and/or Dorea levels in the second gut microbiome sample to Blautia and/or Dorea levels in the first gut microbiome sample, and comparing the first measurement of the kidney disease-related parameter and the second measurement of the kidney disease-related parameter, wherein if the Blautia levels in the second sample are higher than Blautia levels in the first sample and/or Dorea levels in the second sample are higher than Dorea levels in the first sample, and the second measurement of the kidney disease-related parameter is improved compared to the first measurement of the kidney disease-related parameter, the gut microbiome modulating treatment is effective.

63. A method of treating kidney disease comprising administering to an individual in need of such treatment an effective amount of a gut microbiome modulating treatment for harmful kidney disease-related metabolites on a dosage schedule or regimen.

64. The method according to claim 63 wherein the individual in need of such treatment is an individual who is at risk of developing kidney disease based on genetic predisposition, familial history, heredity, lifestyle or one or more kidney disease-related parameters being abnormal.

65. The method according to claim 63 wherein the gut microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; and galactomannan polysaccharides.

66. The method according to claim 65 wherein the probiotic genera is selected from the group consisting of: Bifidobacterium; Staphylococcus; Clostridium; Lactobacilli; Prevotella; Barnsiella; Parasutterella; and combinations thereof.

67. The method according to claim 65 wherein the resistant starch is RS1 , RS2, RS3, RS4, or RS5.

68. The method according to claim 63 wherein the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that increase the availability of resistant starch and/or prebiotics and/or other fermentation substrates to Bifidobacterium in the digestive tract; and antibiotics that target bacteria that inhibit the growth of

Bifidobacterium.

69. The method according to claim 63 wherein the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that increase the availability of resistant starch and/or prebiotics and/or other fermentation substrates to Bifidobacterium in the digestive tract; antibiotics that target bacteria that inhibit the growth of Bifidobacterium ; mixed plant cell wall fibers; beta-glucans; resistant dextrins; resistant maltodextrins; limit dextrins; polydextrose; alginate; pectin polysaccharides; hydroxypropylmethylcellulose; chitin; chondroitin-containing compounds; and glucosamine-containing compounds.

70. The method according to claim 69 wherein the mixed plant cell wall fibers comprise two or more of the following plant cell wall fibers in varying proportions: cellulose, pectin, lignin, beta-glucan, and arabinoxylan regardless of source.

71. The method according to claim 63 wherein the individual in need of such treatment has a kidney disease-related parameter selected from the group consisting of: serum and/or blood indoxyl sulfate levels; stool indoxyl levels; serum and/or blood p- cresol sulfate levels; stool p-cresol levels; stool isobutyrate levels; stool isovalerate levels; serum and/or blood ammonia levels; serum and/or blood urea levels; urea levels in urine; Kt/V values; urea reduction ratios; serum and/or blood urea nitrogen levels; serum and/or blood creatinine levels; creatinine levels in urine; serum and/or blood albumin levels; serum and/or blood protein levels; protein levels in urine; estimated glomerular filtration rates.

72. The method according to claim 63 wherein the gut microbiome modulating compound is resistant potato starch.

73. The method according to claim 63 wherein the effective amount is 2 to 40 g per day of resistant potato starch.

74. The method according to claim 73 wherein the effective amount is administered in one or more doses during the day. 72.

75. The method according to claim 74 wherein the dosage regimen or schedule is from 1 week to 6 months.

76. Use of a gut microbiome modulating treatment for harmful kidney disease- related metabolites for treating kidney disease.

77. The use according to claim 76 wherein the gut microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; and galactomannan polysaccharides.

78. The use according to claim 76 wherein the probiotic genera is selected from the group consisting of: Bifidobacterium; Staphylococcus; Clostridium; Lactobacilli;

Prevotella; Barnsiella; Parasutterella; and combinations thereof.

79. The use according to claim 78 wherein the resistant starch is RS1 , RS2, RS3, RS4, or RS5.

80. The use according to claim 76 wherein the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that increase the availability of resistant starch and/or prebiotics and/or other fermentation substrates to Bifidobacterium in the digestive tract; and antibiotics that target bacteria that inhibit the growth of

Bifidobacterium.

81. The use according to claim 76 wherein the microbiome modulating compound is selected from the group consisting of: resistant potato starch, probiotic genera, species, and strains; prebiotics supporting growth of probiotic genera, species and strains; resistant starch from corn, tapioca, banana, grains, tubers and the like; fructooligosaccharides; galactooligosaccharides; xylooligosaccharides; mannanoligosaccharides; arabinoxylooligosaccharides; arabinogalactan polysaccharides; galactomannan polysaccharides; dietary changes that support the growth of probiotic bacteria; dietary treatments that increase the availability of resistant starch and/or prebiotics and/or other fermentation substrates to Bifidobacterium in the digestive tract; antibiotics that target bacteria that inhibit the growth of Bifidobacterium ; mixed plant cell wall fibers; beta-glucans; resistant dextrins; resistant maltodextrins; limit dextrins; polydextrose; alginate; pectin polysaccharides; hydroxypropylmethylcellulose; chitin; chondroitin-containing compounds; and glucosamine-containing compounds.

82. The use according to claim 81 wherein the mixed plant cell wall fibers comprise two or more of the following plant cell wall fibers in varying proportions: cellulose, pectin, lignin, beta-glucan, and arabinoxylan regardless of source.

83. The use according to claim 76 wherein the gut microbiome modulating compound is resistant potato starch.