TY - JOUR
AU - Coad,, Jane
AB - Dysbiosis is linked to human disease; therefore, gut microbiota modulation strategies provide an attractive means of correcting microbial imbalance to enhance human health. Because diet has a major influence on the composition, diversity, and metabolic capacity of the gut microbiota, numerous dietary intervention studies have been conducted to manipulate the gut microbiota to improve host outcomes and reduce disease risk. Emerging evidence suggests that interindividual variability in gut microbiota and host responsiveness exists, making it difficult to predict gut microbiota and host response to a given dietary intervention. This may, in turn, have implications on the consistency of results among studies and the perceived success or true efficacy of a dietary intervention in eliciting beneficial changes to the gut microbiota and human health. gut microbiota, habitual dietary intake, host outcomes, interindividual variability, responsiveness INTRODUCTION Noncommunicable diseases, such as cardiovascular disease, type II diabetes mellitus (T2DM), chronic respiratory diseases, and cancer, contribute to more than half of all deaths worldwide.1 Major modifiable noncommunicable disease risk factors include smoking, unhealthy dietary intakes (ie, high saturated fat, trans fat, salt, and sugar intakes and low whole-grain, fruit, and vegetable intakes), physical inactivity, and excessive alcohol consumption.2 It has been postulated that the commensal microbes that reside within the gastrointestinal (GI) tract are implicated in human disease. Dysbiotic gut microbiota has been associated with obesity,3 inflammatory bowel disease (IBD),4 T2DM,5 and colon cancers.6 It is difficult to disentangle whether a dysbiotic gut microbiota is causally linked to these diseases or is the result of these diseases or the dietary patterns and medications associated with certain disease states. High-throughput sequencing technology has revolutionized the way in which microbes that colonize the GI tract are analyzed. The main bacterial phylum that are detected in feces are Bacteroidetes and Firmicutes.7 The key genera within the Bacteroidetes phyla include Alistipes, Bacteroides, and Prevotella, and key genera within the Firmicutes phyla include Enterococcus, Lactobacillus, Coprococcus, Dorea, Blautia, Roseburia, Ruminococcus, and Faecalibacterium. Other lower abundance phyla that reside in the human GI tract include Actinobacteria (Bifidobacterium), Verrucomicrobia (Akkermansia), and Proteobacteria (Escherichia). The predominant intestinal bacteria appear to be relatively stable over time in adults.8 However, observational studies have demonstrated inter- and intra-individual variability in gut microbiota composition, which may occur secondarily to external factors such as age,9–11 sex,12,13 antibiotics,14,15 disease,3–6 and diet.16 Numerous human studies have established that diet has a major influence on the structural and functional capacity of the gut microbiota,16–20 but emerging research suggests that gut microbiota response to dietary interventions is highly variable among individuals.21–23 It has been established that individuals harbor microbial taxa that are either responsive or resilient to dietary change, making it very difficult to predict how an individual’s gut microbiota may respond to a given dietary intervention and ultimately how the individual will benefit from the dietary intervention. Previous research suggests that baseline gut microbiota composition20,24,25 and habitual dietary intake26,27 may influence gut microbiota and host response. However, a large proportion of dietary intervention studies that aim to modulate the gut microbiota do not currently take baseline gut microbiota composition and habitual dietary intake into consideration. A greater understanding of the factors implicated in gut microbiota and host responsiveness is needed to establish successful gut microbiota and host outcome modulation strategies. This review examines the evidence for the link between gut microbiota and human disease. This review also explores the role diet plays in modulating the gut microbiota and highlights whether heterogeneous gut microbiota and host response to a given dietary intervention could be influenced by differing baseline gut microbiota composition and habitual dietary intake. GUT MICROBIOTA AND HUMAN DISEASE The human GI tract harbors a complex ecosystem of bacteria, commonly referred to as gut microbiota. A symbiotic host–microbe relationship is believed to benefit human health because the commensal gut microbiota protects its human host against enteropathogenic organism colonization,28,29 modulates the innate and adaptive immune system,29 extracts nutrients from undigested dietary components,29,30 and synthesizes essential vitamins.31 In contrast, intestinal dysbiosis has been suggested to promote or aggravate certain disease states. Gut microbiota development in early life Development of the gut microbiota during a critical period in early life is thought to play a substantial role in the maturation of the immune system and may have an influence on the incidence of atopic, autoimmune, and inflammatory diseases. Modern medical interventions implemented to reduce infant mortality, such as Cesarean sections, infant formula feeding, and antibiotic treatment, may contribute to the rise in immune-mediated diseases observed in economically developed countries. It has been suggested that gut microbiota development is influenced by mode of delivery,32 maternal microbe transfer during gestation,33 environmental microbe exposure,34 use of antibiotics,35 breast or infant formula feeding,36 and complementary feeding practices.35 Breastfed infants have been shown to have lower concentrations of opportunistic bacteria such as Escherichia coli and Clostridium difficile36 but higher Bifidobacterium spp. concentrations37,38 compared with formula-fed infants. Jimenez and colleagues33 used a murine model to investigate whether the maternal gut microbiota transfers to the fetus in utero. Pregnant mice were given milk that contained genetically labeled Enterococcus faecium. After sterile Cesarean section, microbes within the pups’ meconium were analyzed. Genetically labeled E. faecium was able to be cultured from the meconium of pups born to mothers that consumed the genetically labeled milk but not from the control pups. This result suggests that maternal microbiota transmission does occur in mammals. Human studies have suggested that maternal microbiota transmission may occur,39 but to date the most convincing evidence comes from animal studies. Antibiotic use in the first month of life has been shown to reduce the concentrations of bifidobacteria and Bacteroides fragilis group. However, antibiotic use by the mother during pregnancy did not appear to have an influence on the gut microbiota composition of the offspring.35 Early life exposure to antibiotics is also associated with childhood overweight and obesity, which suggests that antibiotic-driven perturbation of the microbial balance at this critical time may have implications on host metabolic functionality.40,41 In one study, the negative association between antibiotic exposure in infancy and childhood overweight was only observed in children born to normal weight mothers. Children born to overweight mothers experienced a slight reduction in childhood overweight if exposed to antibiotics in infancy.40 This is an interesting finding because it suggests that early life antibiotic exposure in offspring of overweight women may reduce the abundance of potentially obesogenic microbiota that may have been passed on via maternal microbiota transmission. Obesity Gnotobiotic mouse models have demonstrated that germ-free mice have reduced adiposity; lower circulating leptin, insulin, and glucose levels; and enhanced glucose tolerance compared with conventional mice that harbor gut microbiota.42 The discovery that germ-free mice fed a Western-style diet did not develop diet-related obesity provided further evidence for an association between gut microbiota and metabolic disease.43 Additionally, greater body fat accretion occurred in germ-free mice that received fecal microbiota transfer from obese human donors than in germ-free mice colonized with the microbiota of lean human donors, further suggesting that the gut microbiota is linked to host metabolism.44 Several mechanisms have been proposed to explain the association between the gut microbiota and host metabolism, including 1) increased energy extraction from fermentable substrates due to the production of short-chain fatty acids (SCFAs)45; 2) altered sensory perception of nutrients such as dietary fat46 and sucrose47; and (3) high-fat dietary intakes leading to an increased production of bacterial lipopolysaccharide (LPS) and, therefore, high concentrations of circulating LPS leading to insulin resistance, endotoxemia, and chronic low-grade inflammation.48,49 The mechanisms that underlie the link between gut microbiota and host metabolism are incompletely understood and need to be explored in greater detail. In humans, obese individuals have a gut microbiota profile that is distinct from the gut microbiota profile of normal-weight individuals. Some studies have demonstrated that obese individuals have a decreased abundance of Bacteroidetes and an increased abundance of Firmicutes.3,50 A study conducted in 98 individuals (30 lean [body mass index (BMI), 18.5–25 kg/m2], 35 overweight [BMI, 25.1–30 kg/m2], and 33 obese [BMI > 30 kg/m2]) demonstrated contrary results.51 Obese and overweight individuals had Firmicutes/Bacteroidetes ratios that favored Bacteroidetes, highlighting the controversy surrounding obesity and the Firmicutes/Bacteroidetes ratio.51 The same study demonstrated that total SCFAs and propionate concentrations in overweight and obese individuals were > 20% higher than that in lean individuals. A number of known propionate producers belong to the phylum Bacteroidetes (ie, Bacteroides and Prevotella), and the joint concentrations of these propionate producers were shown to be significantly (p = 0.001) higher in overweight individuals.51 Another study demonstrated that overweight and obese individuals (n = 37) produced higher amounts of total SCFAs, propionate, butyrate, and acetate compared with lean individuals (n = 52). The Firmicutes/Bacteroidetes ratio did not differ between the 2 groups, but the ratio was shown to significantly (p < 0.0001) correlate with total SCFA concentrations, suggesting that they may be interrelated.52 Microbial diversity also appears to be lower in obese individuals than in lean individuals,50 with lower microbial diversity being shown to be associated with disease risk.53 Even though fairly convincing evidence exists to link dysbiosis with an obese state, uncertainty remains around whether dysbiosis is a cause or consequence of obesity. Type 2 diabetes mellitus Paralleling obesity, rates of T2DM continue to rise worldwide. It is predicted that the number of individuals with T2DM will rise from 382 million (prevalence in 2013) to 592 million by 2035.54 Emerging evidence suggests that microbiota residing within the GI tract influence T2DM risk. It appears that dysbiosis is associated with T2DM, with a depletion in the abundance of several butyrate-producing bacteria (ie, Roseburia spp., Clostridium spp., Eubacterium rectale, and Faecalibacterium prausnitzii) observed in individuals with T2DM.55–58 Multiple bacterial groups have been shown to be positively correlated with plasma glucose concentrations, including the ratio of Bacteroidetes to Firmicutes and the ratio of Bacteroides-Prevotella to Clostridium coccoide-E. rectale.55 Gut microbiota shown to be higher in abundance in individuals with T2DM include Betaproteobacteria, Desulfovibrio, and a number of Lactobacillus spp.55,56,58 In a study of 123 nonobese and 169 obese individuals, a phenotype characterized by metabolic disturbance (ie, increased serum leptin, decreased high-density lipoprotein cholesterol, marked inflammation, insulin resistance, and hyperinsulinemia) was observed in individuals with lower microbial gene richness.53 Therefore, reduced microbial diversity could be a result of insulin resistance and chronic low-grade inflammation, leading to an increased metabolic disease risk. It has been suggested that LPS produced by bacteria stimulates Toll-like receptors, resulting in a proinflammatory response. In mice, increasing the circulating levels of LPS (also known as metabolic endotoxemia) through continuous subcutaneous infusion led to an increase in glycemia, insulinemia, and weight gain comparable in degree to that observed in mice fed a high-fat diet.48 In humans, metabolic endotoxemia has been observed to increase inflammatory markers and insulin resistance.59 The metabolic functionality of gut bacteria may also be implicated in glucose tolerance. De Mello and colleagues60 recruited individuals with impaired glucose tolerance and followed them over a 15-year period to determine whether they developed T2DM. In participants that did not develop T2DM, serum metabolite profiles, particularly 3-indolepropionic acid (a neuroprotective antioxidant) and lipid metabolites, were positively linked to improved insulin production and sensitivity and negatively linked to low-grade inflammation. The researchers suggested that 3-indolepropionic acid could be used as a biomarker of T2DM risk. It has been proposed that SCFAs produced by gut microbiota also affect insulin sensitivity and glucose homeostasis.61 A number of mechanisms of action have been suggested, including activation of intestinal gluconeogenesis by butyrate and propionate62 and an increased secretion of gut hormones from intestinal enteroendocrine cells, such as glucagon-like peptide-1 and peptide YY, which enhance satiety and glucose control.61 Recent research has highlighted that gut microbiota analysis of patients with T2DM may be confounded by antidiabetic drugs such as metformin.63 Metformin treatment has been shown to be associated with an increase in Escherichia spp. and a reduction in Intestinibacter abundance. Therefore, it is important to separate the influence a specific disease has on the gut microbiota from the effect a drug taken to help control a specific disease has.63 Inflammatory bowel disease The incidence and prevalence of IBD in economically developed countries, such as the United States, the United Kingdom, Canada, and New Zealand, continue to rise.64 Inflammatory bowel disease pathogenesis is not fully understood; however, genetic and environmental factors are believed to be involved. Recent work has indicated that interactions between the gut microbiota and host may be involved in the development and exacerbation of IBD.65 It has been hypothesized that IBD may develop due to a dysfunctional immune response that targets the commensal gut microbiota in hosts with genetic susceptibility. In healthy individuals, the interactions between the gut microbiota, mucosal barrier, and immune system are thought to be in equilibrium. In individuals with IBD, this balance is disturbed, leading to dysbiosis, a leaky gut (ie, increased intestinal permeability), and inflammation. Ulcerative colitis (UC) and Crohn’s disease (CD) are the 2 major types of IBD. In individuals with UC, the disease is isolated to the colon, whereas Crohn’s disease can occur anywhere along the GI tract. Microbial dysbiosis is regularly reported in individuals suffering from IBD, particularly in individuals with CD.65 It is, however, unclear whether the changes in gut microbiota are a cause or consequence of the disease. Crohn’s disease is characterized by lower microbial diversity; a reduction in F. prausnitzii, Ruminococcus gnavus, and Bifidobacterium adolescentis; and an increase in E. coli.66 Ulcerative colitis has been associated with lower microbial diversity,67 but gut microbiota profiles in individuals with UC tend to be more similar to those in healthy individuals than to those in individuals with CD.68 A number of studies have demonstrated that bacterial species from the butyrate-producing Clostridium cluster IV and XIVa (ie, F. prausnitzii and Roseburia hominis) are reduced in abundance in individuals with IBD.66,69,70 A number of bacterial genera shown to be in low abundance in individuals with IBD (ie, Bifidobacterium, Lactobacillus, and Faecalibacterium) are known to enhance immune system tolerance and reduce GI inflammation by stimulating CD4+ regulatory T-cell activity,71 downregulating inflammatory cytokines (such as tumor necrosis factor alpha),72 and upregulating anti-inflammatory cytokines (such as interleukin 10).73 It has been reported that a large proportion of individuals with IBD do not respond adequately to current medical treatments, such as immune-modulating therapies. Because the gut microbiota is implicated in IBD pathogenesis, novel microbiota-based therapeutic strategies (ie, prebiotics and probiotics) have the potential to reduce disease incidence and severity. Colorectal cancer The gut microbiota has been recognized as an important component of colon carcinogenesis; therefore, it has been hypothesized that colorectal cancer can be prevented through modulation of the gut microbiota. Studies have shown that individuals with colorectal cancer have a dysbiotic gut microbiota profile compared with healthy individuals.6 Butyrate-producing species have been shown to be under-represented in patients with colorectal cancer.74 Ohigashi and colleagues6 established that colorectal cancer patients (n = 93) also had lower total bacteria, C. coccoides group, Clostridium leptum subgroup, B. fragilis group, Bifidobacterium, Atopobium cluster, total SCFAs, acetate, butyrate, and propionate concentrations compared with healthy individuals (n = 49). Fecal pH was also higher in patients with colorectal cancer compared with healthy individuals with no adenoma.6 Another study provided evidence to suggest that colorectal cancer risk may be mediated by health-promoting (ie, butyrate) and potentially carcinogenic (ie, secondary bile acids) bacterial fermentation metabolites. The microbiota composition and metabolic activity of African Americans with high risk for colorectal cancer (n = 12) were compared with those of matched native Africans with low risk for colorectal cancer (n = 12). The high-risk group was found to have a higher abundance of Bacteroides and potentially pathogenic Proteobacteria and a lower abundance of Prevotella, Faecalibacterium, Succinivibrio, and Oscillospira. The high-risk group had fecal primary and secondary bile acid concentrations that were significantly (p < 0.05) higher compared with the low-risk group. There were also distinctions in production of SCFAs between the 2 groups, with the high-risk group having lower concentrations of acetate, propionate, and butyrate.75 The mechanisms implicated in the carcinogenic potential of a dysbiotic gut microbiota are thought to be linked to the metabolic byproducts the bacteria produce. Gut microbiota provide their human host with a supply of folate, biotin, and butyrate. Folate is involved in DNA repair, replication, and methylation; biotin is involved in gene repression and DNA repair; and butyrate has potent anti-inflammatory and antineoplastic properties. These bacterial metabolites are known to regulate epithelial proliferation; therefore, they have the potential to moderate colorectal cancer risk.76 Because compositional diversity and functional distinctions in gut microbiota exist between healthy individuals and individuals with disease, microbial modulation strategies are an attractive option for enhancing human health and well-being. DIETARY MODULATION OF THE GUT MICROBIOTA Dietary intake has been shown to have a major influence on the structural and functional capacity of the gut microbiota. In mice, dietary change (to a high-fat diet) explains 57% of the variability in gut microbiota composition, with genetics being much less influential.77 It has been hypothesized that the shifts that occur in gut microbiota composition due to dietary change occur because the saccharolytic capacity of the bacterial species that reside within the GI tract varies depending on the genetic capacity of the bacterial communitiy. Additionally, the GI environment (ie, pH, substrate availability, bile salt concentrations) influences the survival of bacterial species. Because the gut microbiota plays a profound role in human health and disease, nutrition and health strategies should aim to target modulation of the gut microbiota. Human observational studies Traditional populations have been studied to investigate the influence of modern lifestyles in economically developed countries on the structural and functional capacity of the gut microbiota.12,17,75,78,79 De Filippo and colleagues17 demonstrated that, in comparison with each other, European children (EC; n = 15) had a higher abundance of Firmicutes and Proteobacteria, whereas children from a rural African village (AC; n = 14) had a higher abundance of Actinobacteria and Bacteroidetes. Xylanibacter, Prevotella, Butyrivibrio, and Treponema are capable of fermenting indigestible carbohydrates and were found exclusively in the AC. Large distinctions in dietary patterns were observed between the AC and EC, which led the researchers to hypothesize that the microbiota of the AC were adapted to the high indigestible carbohydrate content of their diet, leading to the higher abundance of saccharolytic bacteria. The gut microbiota of EC were also shown to be lower in microbial diversity and were less functionally active compared with those of the AC. It is possible that the lower intake of indigestible carbohydrates led to the lower SCFA concentrations observed in EC.17 Similar results were found when structural and functional distinctions in gut microbiota were analyzed in African Americans (AAs; n = 12) and rural native Africans (NAs; n = 12). The dietary differences between the AA and NA groups were similar to what was observed in the de Filippo study, with AAs consuming more animal protein and fat and less dietary fiber than NAs. The most prominent difference in gut microbiota observed was a higher abundance of Prevotella in the NA group and a higher abundance of Bacteroides in the AA group.75 These results correspond with the pioneering enterotype study, which established that individuals could be classified based on 3 predominant bacterial groups—Prevotella, Bacteroides, and Ruminococcus.80 A study published in the same year observed that enterotypes were strongly linked with habitual dietary intakes, with dietary differences being the primary reason for the distinction in enterotypes. High intakes of animal protein and saturated fat were correlated with a Bacteroides enterotype, whereas high intakes of carbohydrates and simple sugars were correlated with a Prevotella enterotype.16 Other distinctive habitual dietary patterns (ie, omnivorous vs vegetarian/vegan diets) are also characterized by differing bacterial profiles and metabolomes.18,81–83 Matijasic and colleagues18 showed there were a number of compositional differences in the gut microbiota of lacto-vegetarians/vegans (n = 31) and omnivores (n = 29) living in Slovenia. Lacto-vegetarians/vegans had a higher relative quantity of Bacteroides-Prevotella, Bacteroides thetaiotaomicron, Clostridium clostridioforme, and F. prausnitzii and a reduced relative quantity of Clostridium cluster XIVa compared with omnivores. The large variance in microbial composition was suggested to be related to the consumption of differing dietary components. Another study, conducted in the United States, found very few gut microbiota composition differences between vegans (n = 15) and omnivores (n = 16), but the investigators did demonstrate that plasma metabolome differences existed, with 25% of the metabolites tested differing between vegans and omnivores. Interestingly, diet appeared to be more predictive of metabolome production than gut microbiota composition in this cohort.83 As previously noted, commensal microbes that reside within the GI tract are implicated in human disease. Therefore, because habitual dietary intakes have a major impact on the composition of the gut microbiota, it is plausible that dietary interventions could be used to modulate the gut microbiota to reduce disease risk. Human dietary intervention studies Dietary interventions that aim to elicit changes in the gut microbiota provide an exciting prospect for disease prevention and management. Extensive research has demonstrated that dietary interventions can elicit changes in gut microbiota composition and function. Pronounced short-term changes in macronutrient intake have been shown to result in dramatic shifts in gut microbiota composition within 24 hours. David and colleagues19 initiated a 5-day dietary intervention consisting of either a plant-based diet of grains, legumes, fruits and vegetables or an animal-based diet of meat, eggs, and cheese in 10 healthy individuals. The animal-based diet had the most pronounced impact on the bacterial taxa, particularly for bile-tolerant bacterial groups (ie, Bilophila wadsworthia, Alistipes putredinis, and Bacteroides spp.) and amino-acid fermentation-specific SCFA production (ie, isovalerate and isobutyrate). A positive correlation between saccharolytic bacteria (ie, Roseburia spp., E. rectale, and F. prausnitzii) and carbohydrate fermentation byproducts (ie, acetate and butyrate) was demonstrated in those assigned to the plant-based diet. Dietary fibers are nondigestible plant oligo- and polysaccharides, such as nonstarch polysaccharides, resistant starch, beta-glucan, and inulin-type fructans, that are found in whole grains, legumes, nuts and seeds, fruits, and vegetables. A diet that is high in fiber has been shown to have a number of health benefits.84 The exact mechanisms are not fully understood; however, research suggests that the benefits of dietary fiber on human pathophysiology may be mediated by the gut microbiota.85,86 In germ-free mice inoculated with human microbiota, dietary fiber deficiency led to microbial utilization of host-secreted mucin as a source of energy because diet-derived fermentable substrates were unavailable. Colonic mucin degradation led to intestinal barrier dysfunction, which promoted microbial-induced colitis.87 Long-term low fiber consumption in mice has also been shown to lead to intergenerational loss of certain bacterial species that are not restored by increasing the fiber content of the diet in future generations. This suggests that “extinction” of potentially beneficial microbes may occur when the GI environment is depleted of fermentable carbohydrates.88 A study conducted in 82 individuals demonstrated that in women total fiber and fiber from fruit, vegetables, and grains were associated with overall gut microbiota composition, whereas in men the only association was for bean fiber and gut microbiota composition. Additionally, intakes of fiber from fruit and vegetables was shown to cluster with the class Clostridia, whereas intakes of fiber from beans clustered with members of the phyla Actinobacteria.85 Diets low in indigestible short-chain carbohydrates, also known as low fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP) diets, have been used as a dietary therapy for individuals with irritable bowel syndrome (IBS). A reduction in the FODMAP content of the diet is believed to reduce the production of microbial fermentation byproducts, such as gas, helping to alleviate common IBS symptoms (ie, flatulence and bloating). Microbial production of SCFAs is thought to help reduce diarrhea, another common symptom of IBS, because SCFAs help stimulate fluid absorption in the colon.89 A number of low FODMAP dietary intervention studies in individuals with IBS have been conducted to demonstrate whether the reduction in symptoms is associated with changes in gut microbiota. Considerable changes in gut microbiota have been shown after a low FODMAP diet.90–92 Halmos and colleagues90 observed lower total bacteria concentrations and Clostridium cluster XIVa and Akkermansia muciniphila abundance in individuals consuming a low FODMAP diet compared with a typical Australian diet. Stool pH was higher after the low FODMAP diet, but there were no differences in stool SCFA concentrations between the 2 diet types. The low FODMAP diet reduced GI symptoms irrespective of changes in stool SCFA concentrations.93 Conversely, increases in dietary fiber intake in healthy individuals have led to changes in the intestinal microbiota. Five-day supplementation with 10 or 40 g/day of dietary fiber in a group of 19 healthy individuals led to modest differences in gut microbiota composition between the low and high dietary fiber supplemented groups.20 Over-representation of microbial genes associated with glycan and lipid metabolism and a downregulation of genes associated with mucin degradation were seen in individuals whose diets were supplemented with 40 g/day of dietary fiber.20 These results suggest that dietary fiber–associated changes in the functional capacity of the microbiota may occur irrespective of changes in microbiota community structure. A prebiotic is “a substrate that is selectively utilized by host microorganisms conferring a health benefit”.94 Prebiotics have the potential to be used to increase beneficial bacteria that are low in abundance in individuals with increased disease risk. Established prebiotics include inulin-type fructans (eg, inulin, oligofructose, and fructo-oligosaccharides), galacto-oligosaccharides, and lactulose. Targeting specific bacterial taxa requires an understanding of what constitutes a beneficial bacterial profile as well as a broad appreciation of the substrates used by beneficial bacteria. A number of prebiotic intervention studies in humans have been conducted to demonstrate the influence of a given fermentable substrate (ie, prebiotic) on gut microbiota composition and host outcomes. A large proportion of prebiotic intervention studies have focused on eliciting changes in bifidobacteria because high levels of these bacteria are considered to represent a healthy microbial consortium.95–98 Other bacterial groups have also been shown to increase as a result of prebiotic interventions, including Lactobacillus97 and Faecalibacterium,99 but very few studies have demonstrated what influence prebiotics have on the entire community. Holscher and colleagues100 used high-throughput sequencing technology to determine the impact of 5 or 7.5 g/day of agave inulin on the entire microbial community of 29 healthy adults. In addition to a significant increase in Bifidobacterium relative abundance (p < 0.01), a reduction in Ruminococcus (p < 0.01) and Desulfovibrio relative abundances (p = 0.01) were demonstrated, providing a clearer picture of the response of the entire microbial community to agave inulin. A number of studies that have demonstrated that a given dietary intervention elicits changes in the structure and functional capacity of the gut microbiota have also shown that gut microbiota changes are associated with improvements in host health outcomes. A study conducted in 81 metabolically healthy individuals aged between 40 and 65 years determined whether a diet rich in whole grains was able to change the gut microbiota and improve host immune response.101 Participants were instructed to consume a Western-style diet high in refined grains for 2 weeks to help minimize the influence of their habitual diet on the results. For the following 6 weeks, half of the participants continued on the diet high in refined grains (8 g of dietary fiber per 1000 kcal), and the other half commenced a diet rich in whole grains (16 g of dietary fiber per 1000 kcal). The calorie content of the diets was personalized to ensure each participant’s weight was stable throughout the trial. Individuals consuming the diet high in whole grains experienced an increase in stool weight and frequency. A small but significant increase in total SCFAs (p = 0.05) and acetate (p = 0.02) were observed in the group that consumed a diet high in whole grains compared with the group that consumed a diet high in refined grains. A modest increase in the acetate-producing genera Lachnospira and a reduction in the proinflammatory genera Enterobacteriaceae were also observed. The diet high in whole grains also had an influence on effector memory T cells and acute innate immune response.101 Dewulf and colleagues102 demonstrated that a 3-month inulin-type fructan prebiotic intervention in 30 obese women led to significant (p < 0.05) increases in bifidobacteria and F. prausnitzii concentrations. The increases in these bacterial groups were shown to be negatively associated with changes in LPS, a proinflammatory byproduct of bacterial fermentation. This result is interesting because high plasma concentrations of LPS are thought to be associated with the chronic low-grade inflammation characteristic of obesity.48 The prebiotic also appeared to reduce fat mass and led to a reduction in post–oral glucose tolerance test glycemia compared with the placebo.102 INTERINDIVIDUAL VARIABILITY IN GUT MICROBIOTA AND HOST RESPONSIVENESS TO DIETARY INTERVENTIONS Even though various dietary intervention studies have elicited changes in both host outcomes and the gut microbiota, mounting evidence suggests that individuals are heterogeneous when it comes to gut microbiota and host responsiveness to a given dietary intervention. It is possible that gut microbiota and host resilience to dietary change may be related to interindividual variability in baseline gut microbiota composition and habitual dietary intake. Individualized gut microbiota resilience may actually play a more pronounced role in gut microbiota response than dietary change itself.21 It appears that the responsive bacterial taxa differ among individuals.23,103–105 Presently, it is very difficult to predict how the gut microbiota and host will respond to a given dietary intervention. Individualized gut microbiota and host responsiveness have the potential to influence study results and impact reproducibility among studies. Identification and a greater understanding of the factors that influence individualized responsiveness may help ensure that the true efficacy of a given dietary intervention is established. Response of gut microbiota to dietary interventions Influence of baseline gut microbiota composition. The emerging concept of individualized gut microbiota responsiveness indicates a link between baseline gut microbiota composition and response to a dietary intervention. Certain baseline gut microbiota profiles may express an inherent resistance to change and increase their resilience toward dietary modification. Table 120–27,96,100,103–111,122 summarizes a sample of human intervention studies that have observed varying gut microbiota responses based on differing baseline gut microbiota profiles or habitual dietary intakes. The first studies to observe individualized gut microbiota responses established that baseline bifidobacteria concentrations affected the magnitude of change that occurs in bifidobacteria after a prebiotic intervention. Tuohy and colleagues106 were the first researchers to demonstrate that individuals with lower baseline bifidobacteria concentrations experience a more pronounced increase in bifidobacteria after an inulin intervention. This result has been replicated in a number of prebiotic intervention studies.96,107–109 One study conducted in 31 healthy individuals demonstrated that biscuits containing fructo-oligosaccharides (6.6 g/d) and guar gum (3.4 g/d) stimulated bifidobacteria growth only in individuals with a baseline bifidobacteria concentration of < 9.3 log10 cells/g.24 However, one study demonstrated that baseline bifidobacteria populations among individuals varied considerably (ie, below detectable levels to 4.6% of total bacterial genes) but no correlation existed between baseline bifidobacteria and change in bifidobacteria after an inulin-type fructan prebiotic intervention.110 The heterogeneity in results seen between this study and the studies previously noted may be related to the small sample size used (n = 12) or the reporting of the relative proportion of bifidobacteria (ie, percentage of total bacterial genes) rather than absolute bifidobacteria concentrations. Table 1 Observed differences in baseline gut microbiota composition and function and/or dietary intake and host characteristics between responders and nonresponders in studies that demonstrated differing gut microbiota responses to a dietary intervention Gut microbiota responsiveness Reference Study information Responsiveness definition Observed baseline differences Observed differences in gut microbiota response Participants Intervention Analysis method Gut microbiota composition and function Dietary intake and host characteristics 10 healthy 100 g of cracker containing 33 g of RS for 3 wk: either RS2 Hi-Maize or RS4 Fibersym 454 pyrosequencing, DGGE, and qPCR Magnitude of change in gut microbiota composition Not investigated Not investigated The magnitude of change varied considerably among individuals. None of the community shifts were observed in all participants Martínez et al. (2010)104 18 healthy 4 wk of each of the following: chews with 0 g, 2.5 g, 5 g, and 10 g/d of GOS 454 pyrosequencing Change in major bacterial groups analyzed: Bifidobacterium and Bacteroides No baseline differences in gut microbiota composition between R and NR Not investigated R: Significant change in at least 1 of the major bacterial groups analyzed after the 5 g and/or 10 g/d GOS NR: Bacterial groups analyzed were unaffected by 5 g and 10 g/d GOS Davis et al. (2011)23 14 overweight mena 1 wk M diet, 3 wk high RS, 3 wk high NSP and 3 wk weight loss diet (high protein, low CHO) Pyrosequencing, DGGE, and qPCR Distance between baseline and postintervention samples on a PCoA Differing baseline gut microbiota composition between R and NR; did not elaborate Not investigated R: Postintervention samples a distance away from the baseline sample on a PCoA NR: Postintervention and baseline samples cluster on a PCoA Walker et al. (2011)105 31 healthy Biscuits containing 6.6 g/d FOS and 3.4 g/d guar gum for 21 d FISH Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR. NR had a baseline bifidobacteria ≥9.3 log10 cells/g Not investigated R: Increase in bifidobacteria NR: No change in bifidobacteria Tuohy et al. (2001)24 9 healthy 8 g/d of IN for 2 wk FISH Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Tuohy et al. (2001)106 8 healthy per group Consumed either 2.5 g, 5 g, 7.5 g, or 10 g/d of SC-FOS, SBOS, GOS, or RS for 7 d (16 groups) Culture based Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Bouhnik et al. (2004)107 30 healthy 2 wk 5 g/d of IN, 1 wk washout and 2 wk of 8 g/d of IN FISH Change in bifidobacteria Significantly lower baseline bifidobacteria levels in R compared with NR for 5 and 8 g/d of IN Not investigated R: Increase in bifidobacteria NR: No change in bifidobacteria Kolida et al. (2007)108 39 healthy 5 g/d IN for 4 wk Culture based Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Bouhnik et al. (2007)96 19 healthy For 4 wk of Lactulose 10 g twice a day OR Synergy 1 (FOS: IN) 10 g twice a day qPCR and DGGE Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria de Preter et al. (2007)109 12 healthy 5 g Synergy 1 (FOS: IN) twice a day for 3 wk qPCR Change in bifidobacteria No significant difference in baseline bifidobacteria between R and NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Fuller et al. (2007)110 19 healthy 10 g/d or 40 g/d of dietary fiber (crossover design) with a 2 wk washout phase 454 pyrosequencing and qPCR Microbial stability R: Low microbial diversity (OTU numbers) NR: High microbial diversity (OTU numbers) NR had a higher diversity of vegetable intake compared with R R: Higher microbial change NR: Higher microbial stability Tap et al. (2015)20 45 overweight and obeseb 6 wk of calorie-restriction, high protein, low glycemic index diet followed by a 6 wk M diet SOLiD sequencing Microbial gene richness stability R: LGR NR: HGR R fewer fruit, vegetables, and fish products and there was a trend toward lower dietary fiber intakes compared with NR R: Significant increase in gene richness; however gene richness was still significantly lower than in NR NR: No change in gene richness Cotillard et al. (2013)111 14 overweight mena 1 wk M diet, 3 wk high RS, and 3 wk high NSP and 3 wk weight loss diet (high protein, low CHO) HITChip and qPCR Distance between baseline and postintervention samples on PCoA R had significantly lower microbial diversity (inverse Simpson index) than NR No difference in habitual diets between R and NR. NSP intake correlated with microbial diversity in R but not NR R: Postintervention samples a distance away from the baseline sample on a PCoA NR: Postintervention and baseline samples cluster on a PCoA Salonen et al. (2014)21 45 overweight and obeseb 6 wk of calorie-restriction, high protein, low glycemic index diet followed by a 6 wk M diet SOLiD sequencing Change in gut microbiota composition Baseline differences in Bifidobacterium adolescentis, Faecalibacterium prausnitzii, and Eubacterium rectale between R and NR groups Not investigated R: Increase in Bacteroides thetaiotaomicron and a decrease in Lactobacillus reuteri and F. prausnitzii NR: Decrease in L. reuteri Shoaie et al. (2015)103 46 healthy 4 wks 25 g/d NSP, 2 wk washout, and 4 wk 25 g/d NSP + 22 g/d RS diet SCFA only: Gas chromatography Increase in butyrate concentration Baseline butyrate concentratio differences; grouped into quartiles NR (highest quartile) had higher BMI, energy, and protein intakes compared with R (lowest quartile). Fiber intake did not differ R: Increase in butyrate concentration NR: Decrease or no change in butyrate concentration McOrist et al. (2011)22 14 healthy women 7 wk of FMP; 2 × 4 oz./d 16S rRNA bacterial gene sequencing Persistence of Lactococcus lactis strains after FMP stopped R:Lactococcus carrier Higher abundance of Barnesiellaceae, Odoribacteraceae, and Clostridiaceae NR: Lactococcus noncarrier. Lower abundance of Barnesiellaceae, Odoribacteraceae, and Clostridiaceae Not investigated R: Persistence of L. lactis (probiotic within FMP) after FMP stopped (n = 5) NR: No L. lactis persistence after FMP stopped (n = 9) Significantly higher weighted UniFrac distance after 1 wk of FMP in R compared with NR Zhang et al. (2016)25 29 healthy 3 × 3 wk interventions with a 1 wk washout phase between each intervention phase. Chocolate chews with 0, 5, or 7.5 g of agave IN 16S rRNA Illumina sequencing Change in Bifidobacterium relative abundance Not investigated Higher dietary fiber intakes were associated with increased butyrate production and a trend toward higher Bifidobacterium R: Significant increase in Bifidobacterium relative abundance NR: No significant increase in Bifidobacterium relative abundance Holscher et al. (2015)100 10 healthy donors + 30 gnotobiotic mice Fecal samples from 5 CRON individuals and 5 AMER individuals. Inoculated gnotobiotic mice were fed a representative CRON or AMER diet 16S rRNA bacterial gene sequencing Shift in dietary pattern associated bacteria R: Higher abundance of Bacteroides species (cellulosilyticus, thetaiotaomicron), Parabacteroides goldsteinii, and Allstipes putredinis NR: Higher abundance of Dorea longicatena, Blautia, Coporoccus comes, and Clostridium clostridioforme CRON donors consumed 42.1% less calories, 48.6% less fat, 33.5% less carbohydrates, 37.6% less total protein, and 60.8% less protein from animal sources compared with AMER donors R: CRON-inoculated mice had a shift toward the CRON or AMER dietary pattern associated bacterial profile to match the diet (CRON or AMER) consumed NR: AMER-inoculated mice had a weaker shift toward a CRON-associated bacterial profile on the CRON diet Griffin et al. (2017)26 22 healthy 50 g/d palm date for 3 wk and 3.9 g maltodextrin and 33.19g dextrose per day for 3 wk with a 2 wk washout period between interventions FISH Change in gut microbiota composition R: Lower baseline Bacteroides concentration NR: Higher baseline Bacteroides concentration R had a higher dietary fiber intake (18.5 g/d) then NR (6 g/d) R: Significant increase in total bacteria, Bacteroides, Lactobacillus/Enterococcus, C.coccoides-E. rectale, Ruminococcus bromii + flavefaciens, and Roseburia + E. rectale groups NR: No change in gut microbiota Eid et al. (2015)27 34 healthy 16 g/d of inulin-type fructan prebiotic for 3 wk 16S rRNA Illumina sequencing Change in gut microbiota composition R: Lower abundance of unknown genus of Lachnospiraceae NR: Higher abundance of unknown genus of Lachnospiraceae R had higher dietary fiber, energy, fat, PUFA, MUFA, CHO, fiber per 1000 kJ, vegetable, fruit, nut, and seed intakes than NR R: Increase in Bifidobacterium and Faecalibacterium, decrease in Coprococcus, Dorea, and Ruminococcus NR: Increase in Bifidobacterium only Healey et al. (2017) (in press)122 Gut microbiota responsiveness Reference Study information Responsiveness definition Observed baseline differences Observed differences in gut microbiota response Participants Intervention Analysis method Gut microbiota composition and function Dietary intake and host characteristics 10 healthy 100 g of cracker containing 33 g of RS for 3 wk: either RS2 Hi-Maize or RS4 Fibersym 454 pyrosequencing, DGGE, and qPCR Magnitude of change in gut microbiota composition Not investigated Not investigated The magnitude of change varied considerably among individuals. None of the community shifts were observed in all participants Martínez et al. (2010)104 18 healthy 4 wk of each of the following: chews with 0 g, 2.5 g, 5 g, and 10 g/d of GOS 454 pyrosequencing Change in major bacterial groups analyzed: Bifidobacterium and Bacteroides No baseline differences in gut microbiota composition between R and NR Not investigated R: Significant change in at least 1 of the major bacterial groups analyzed after the 5 g and/or 10 g/d GOS NR: Bacterial groups analyzed were unaffected by 5 g and 10 g/d GOS Davis et al. (2011)23 14 overweight mena 1 wk M diet, 3 wk high RS, 3 wk high NSP and 3 wk weight loss diet (high protein, low CHO) Pyrosequencing, DGGE, and qPCR Distance between baseline and postintervention samples on a PCoA Differing baseline gut microbiota composition between R and NR; did not elaborate Not investigated R: Postintervention samples a distance away from the baseline sample on a PCoA NR: Postintervention and baseline samples cluster on a PCoA Walker et al. (2011)105 31 healthy Biscuits containing 6.6 g/d FOS and 3.4 g/d guar gum for 21 d FISH Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR. NR had a baseline bifidobacteria ≥9.3 log10 cells/g Not investigated R: Increase in bifidobacteria NR: No change in bifidobacteria Tuohy et al. (2001)24 9 healthy 8 g/d of IN for 2 wk FISH Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Tuohy et al. (2001)106 8 healthy per group Consumed either 2.5 g, 5 g, 7.5 g, or 10 g/d of SC-FOS, SBOS, GOS, or RS for 7 d (16 groups) Culture based Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Bouhnik et al. (2004)107 30 healthy 2 wk 5 g/d of IN, 1 wk washout and 2 wk of 8 g/d of IN FISH Change in bifidobacteria Significantly lower baseline bifidobacteria levels in R compared with NR for 5 and 8 g/d of IN Not investigated R: Increase in bifidobacteria NR: No change in bifidobacteria Kolida et al. (2007)108 39 healthy 5 g/d IN for 4 wk Culture based Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Bouhnik et al. (2007)96 19 healthy For 4 wk of Lactulose 10 g twice a day OR Synergy 1 (FOS: IN) 10 g twice a day qPCR and DGGE Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria de Preter et al. (2007)109 12 healthy 5 g Synergy 1 (FOS: IN) twice a day for 3 wk qPCR Change in bifidobacteria No significant difference in baseline bifidobacteria between R and NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Fuller et al. (2007)110 19 healthy 10 g/d or 40 g/d of dietary fiber (crossover design) with a 2 wk washout phase 454 pyrosequencing and qPCR Microbial stability R: Low microbial diversity (OTU numbers) NR: High microbial diversity (OTU numbers) NR had a higher diversity of vegetable intake compared with R R: Higher microbial change NR: Higher microbial stability Tap et al. (2015)20 45 overweight and obeseb 6 wk of calorie-restriction, high protein, low glycemic index diet followed by a 6 wk M diet SOLiD sequencing Microbial gene richness stability R: LGR NR: HGR R fewer fruit, vegetables, and fish products and there was a trend toward lower dietary fiber intakes compared with NR R: Significant increase in gene richness; however gene richness was still significantly lower than in NR NR: No change in gene richness Cotillard et al. (2013)111 14 overweight mena 1 wk M diet, 3 wk high RS, and 3 wk high NSP and 3 wk weight loss diet (high protein, low CHO) HITChip and qPCR Distance between baseline and postintervention samples on PCoA R had significantly lower microbial diversity (inverse Simpson index) than NR No difference in habitual diets between R and NR. NSP intake correlated with microbial diversity in R but not NR R: Postintervention samples a distance away from the baseline sample on a PCoA NR: Postintervention and baseline samples cluster on a PCoA Salonen et al. (2014)21 45 overweight and obeseb 6 wk of calorie-restriction, high protein, low glycemic index diet followed by a 6 wk M diet SOLiD sequencing Change in gut microbiota composition Baseline differences in Bifidobacterium adolescentis, Faecalibacterium prausnitzii, and Eubacterium rectale between R and NR groups Not investigated R: Increase in Bacteroides thetaiotaomicron and a decrease in Lactobacillus reuteri and F. prausnitzii NR: Decrease in L. reuteri Shoaie et al. (2015)103 46 healthy 4 wks 25 g/d NSP, 2 wk washout, and 4 wk 25 g/d NSP + 22 g/d RS diet SCFA only: Gas chromatography Increase in butyrate concentration Baseline butyrate concentratio differences; grouped into quartiles NR (highest quartile) had higher BMI, energy, and protein intakes compared with R (lowest quartile). Fiber intake did not differ R: Increase in butyrate concentration NR: Decrease or no change in butyrate concentration McOrist et al. (2011)22 14 healthy women 7 wk of FMP; 2 × 4 oz./d 16S rRNA bacterial gene sequencing Persistence of Lactococcus lactis strains after FMP stopped R:Lactococcus carrier Higher abundance of Barnesiellaceae, Odoribacteraceae, and Clostridiaceae NR: Lactococcus noncarrier. Lower abundance of Barnesiellaceae, Odoribacteraceae, and Clostridiaceae Not investigated R: Persistence of L. lactis (probiotic within FMP) after FMP stopped (n = 5) NR: No L. lactis persistence after FMP stopped (n = 9) Significantly higher weighted UniFrac distance after 1 wk of FMP in R compared with NR Zhang et al. (2016)25 29 healthy 3 × 3 wk interventions with a 1 wk washout phase between each intervention phase. Chocolate chews with 0, 5, or 7.5 g of agave IN 16S rRNA Illumina sequencing Change in Bifidobacterium relative abundance Not investigated Higher dietary fiber intakes were associated with increased butyrate production and a trend toward higher Bifidobacterium R: Significant increase in Bifidobacterium relative abundance NR: No significant increase in Bifidobacterium relative abundance Holscher et al. (2015)100 10 healthy donors + 30 gnotobiotic mice Fecal samples from 5 CRON individuals and 5 AMER individuals. Inoculated gnotobiotic mice were fed a representative CRON or AMER diet 16S rRNA bacterial gene sequencing Shift in dietary pattern associated bacteria R: Higher abundance of Bacteroides species (cellulosilyticus, thetaiotaomicron), Parabacteroides goldsteinii, and Allstipes putredinis NR: Higher abundance of Dorea longicatena, Blautia, Coporoccus comes, and Clostridium clostridioforme CRON donors consumed 42.1% less calories, 48.6% less fat, 33.5% less carbohydrates, 37.6% less total protein, and 60.8% less protein from animal sources compared with AMER donors R: CRON-inoculated mice had a shift toward the CRON or AMER dietary pattern associated bacterial profile to match the diet (CRON or AMER) consumed NR: AMER-inoculated mice had a weaker shift toward a CRON-associated bacterial profile on the CRON diet Griffin et al. (2017)26 22 healthy 50 g/d palm date for 3 wk and 3.9 g maltodextrin and 33.19g dextrose per day for 3 wk with a 2 wk washout period between interventions FISH Change in gut microbiota composition R: Lower baseline Bacteroides concentration NR: Higher baseline Bacteroides concentration R had a higher dietary fiber intake (18.5 g/d) then NR (6 g/d) R: Significant increase in total bacteria, Bacteroides, Lactobacillus/Enterococcus, C.coccoides-E. rectale, Ruminococcus bromii + flavefaciens, and Roseburia + E. rectale groups NR: No change in gut microbiota Eid et al. (2015)27 34 healthy 16 g/d of inulin-type fructan prebiotic for 3 wk 16S rRNA Illumina sequencing Change in gut microbiota composition R: Lower abundance of unknown genus of Lachnospiraceae NR: Higher abundance of unknown genus of Lachnospiraceae R had higher dietary fiber, energy, fat, PUFA, MUFA, CHO, fiber per 1000 kJ, vegetable, fruit, nut, and seed intakes than NR R: Increase in Bifidobacterium and Faecalibacterium, decrease in Coprococcus, Dorea, and Ruminococcus NR: Increase in Bifidobacterium only Healey et al. (2017) (in press)122 Abbreviations: AMER, American diet with no calorie-restriction; BMI, body mass index; CHO, carbohydrate; CRON, calorie-restricted adequate nutrition diet; DGGE, denaturing gradient gel electrophoresis; FOS, fructo-oligosaccharide; FISH, fluorescence in situ hybridization; FMP, fermented milk product; GOS, galacto-oligosaccharide; HGR, high bacterial gene richness; HITChip, human intestinal tract chip; IN, inulin; LGR, low bacterial gene richness; M, maintenance; MUFA, monounsaturated fatty acids; NR, nonresponder; NSP, nonstarch polysaccharide; OUT, operational taxonomic unit; PCoA, principal coordinate analysis; PUFA, polyunsaturated fatty acids; qPCR, quantitative polymerase chain reaction; R, responder; RS, resistant starch; rRNA, ribosomal RNA; SCFA, short-chain fatty acid, SC-FOS, short-chain fructo-oligosaccharide; SBOS, soybean oligosaccharide; SOLiD, sequencing of oligonucleotides by ligation. a Same study cohort. b Same study cohort. Table 1 Observed differences in baseline gut microbiota composition and function and/or dietary intake and host characteristics between responders and nonresponders in studies that demonstrated differing gut microbiota responses to a dietary intervention Gut microbiota responsiveness Reference Study information Responsiveness definition Observed baseline differences Observed differences in gut microbiota response Participants Intervention Analysis method Gut microbiota composition and function Dietary intake and host characteristics 10 healthy 100 g of cracker containing 33 g of RS for 3 wk: either RS2 Hi-Maize or RS4 Fibersym 454 pyrosequencing, DGGE, and qPCR Magnitude of change in gut microbiota composition Not investigated Not investigated The magnitude of change varied considerably among individuals. None of the community shifts were observed in all participants Martínez et al. (2010)104 18 healthy 4 wk of each of the following: chews with 0 g, 2.5 g, 5 g, and 10 g/d of GOS 454 pyrosequencing Change in major bacterial groups analyzed: Bifidobacterium and Bacteroides No baseline differences in gut microbiota composition between R and NR Not investigated R: Significant change in at least 1 of the major bacterial groups analyzed after the 5 g and/or 10 g/d GOS NR: Bacterial groups analyzed were unaffected by 5 g and 10 g/d GOS Davis et al. (2011)23 14 overweight mena 1 wk M diet, 3 wk high RS, 3 wk high NSP and 3 wk weight loss diet (high protein, low CHO) Pyrosequencing, DGGE, and qPCR Distance between baseline and postintervention samples on a PCoA Differing baseline gut microbiota composition between R and NR; did not elaborate Not investigated R: Postintervention samples a distance away from the baseline sample on a PCoA NR: Postintervention and baseline samples cluster on a PCoA Walker et al. (2011)105 31 healthy Biscuits containing 6.6 g/d FOS and 3.4 g/d guar gum for 21 d FISH Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR. NR had a baseline bifidobacteria ≥9.3 log10 cells/g Not investigated R: Increase in bifidobacteria NR: No change in bifidobacteria Tuohy et al. (2001)24 9 healthy 8 g/d of IN for 2 wk FISH Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Tuohy et al. (2001)106 8 healthy per group Consumed either 2.5 g, 5 g, 7.5 g, or 10 g/d of SC-FOS, SBOS, GOS, or RS for 7 d (16 groups) Culture based Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Bouhnik et al. (2004)107 30 healthy 2 wk 5 g/d of IN, 1 wk washout and 2 wk of 8 g/d of IN FISH Change in bifidobacteria Significantly lower baseline bifidobacteria levels in R compared with NR for 5 and 8 g/d of IN Not investigated R: Increase in bifidobacteria NR: No change in bifidobacteria Kolida et al. (2007)108 39 healthy 5 g/d IN for 4 wk Culture based Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Bouhnik et al. (2007)96 19 healthy For 4 wk of Lactulose 10 g twice a day OR Synergy 1 (FOS: IN) 10 g twice a day qPCR and DGGE Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria de Preter et al. (2007)109 12 healthy 5 g Synergy 1 (FOS: IN) twice a day for 3 wk qPCR Change in bifidobacteria No significant difference in baseline bifidobacteria between R and NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Fuller et al. (2007)110 19 healthy 10 g/d or 40 g/d of dietary fiber (crossover design) with a 2 wk washout phase 454 pyrosequencing and qPCR Microbial stability R: Low microbial diversity (OTU numbers) NR: High microbial diversity (OTU numbers) NR had a higher diversity of vegetable intake compared with R R: Higher microbial change NR: Higher microbial stability Tap et al. (2015)20 45 overweight and obeseb 6 wk of calorie-restriction, high protein, low glycemic index diet followed by a 6 wk M diet SOLiD sequencing Microbial gene richness stability R: LGR NR: HGR R fewer fruit, vegetables, and fish products and there was a trend toward lower dietary fiber intakes compared with NR R: Significant increase in gene richness; however gene richness was still significantly lower than in NR NR: No change in gene richness Cotillard et al. (2013)111 14 overweight mena 1 wk M diet, 3 wk high RS, and 3 wk high NSP and 3 wk weight loss diet (high protein, low CHO) HITChip and qPCR Distance between baseline and postintervention samples on PCoA R had significantly lower microbial diversity (inverse Simpson index) than NR No difference in habitual diets between R and NR. NSP intake correlated with microbial diversity in R but not NR R: Postintervention samples a distance away from the baseline sample on a PCoA NR: Postintervention and baseline samples cluster on a PCoA Salonen et al. (2014)21 45 overweight and obeseb 6 wk of calorie-restriction, high protein, low glycemic index diet followed by a 6 wk M diet SOLiD sequencing Change in gut microbiota composition Baseline differences in Bifidobacterium adolescentis, Faecalibacterium prausnitzii, and Eubacterium rectale between R and NR groups Not investigated R: Increase in Bacteroides thetaiotaomicron and a decrease in Lactobacillus reuteri and F. prausnitzii NR: Decrease in L. reuteri Shoaie et al. (2015)103 46 healthy 4 wks 25 g/d NSP, 2 wk washout, and 4 wk 25 g/d NSP + 22 g/d RS diet SCFA only: Gas chromatography Increase in butyrate concentration Baseline butyrate concentratio differences; grouped into quartiles NR (highest quartile) had higher BMI, energy, and protein intakes compared with R (lowest quartile). Fiber intake did not differ R: Increase in butyrate concentration NR: Decrease or no change in butyrate concentration McOrist et al. (2011)22 14 healthy women 7 wk of FMP; 2 × 4 oz./d 16S rRNA bacterial gene sequencing Persistence of Lactococcus lactis strains after FMP stopped R:Lactococcus carrier Higher abundance of Barnesiellaceae, Odoribacteraceae, and Clostridiaceae NR: Lactococcus noncarrier. Lower abundance of Barnesiellaceae, Odoribacteraceae, and Clostridiaceae Not investigated R: Persistence of L. lactis (probiotic within FMP) after FMP stopped (n = 5) NR: No L. lactis persistence after FMP stopped (n = 9) Significantly higher weighted UniFrac distance after 1 wk of FMP in R compared with NR Zhang et al. (2016)25 29 healthy 3 × 3 wk interventions with a 1 wk washout phase between each intervention phase. Chocolate chews with 0, 5, or 7.5 g of agave IN 16S rRNA Illumina sequencing Change in Bifidobacterium relative abundance Not investigated Higher dietary fiber intakes were associated with increased butyrate production and a trend toward higher Bifidobacterium R: Significant increase in Bifidobacterium relative abundance NR: No significant increase in Bifidobacterium relative abundance Holscher et al. (2015)100 10 healthy donors + 30 gnotobiotic mice Fecal samples from 5 CRON individuals and 5 AMER individuals. Inoculated gnotobiotic mice were fed a representative CRON or AMER diet 16S rRNA bacterial gene sequencing Shift in dietary pattern associated bacteria R: Higher abundance of Bacteroides species (cellulosilyticus, thetaiotaomicron), Parabacteroides goldsteinii, and Allstipes putredinis NR: Higher abundance of Dorea longicatena, Blautia, Coporoccus comes, and Clostridium clostridioforme CRON donors consumed 42.1% less calories, 48.6% less fat, 33.5% less carbohydrates, 37.6% less total protein, and 60.8% less protein from animal sources compared with AMER donors R: CRON-inoculated mice had a shift toward the CRON or AMER dietary pattern associated bacterial profile to match the diet (CRON or AMER) consumed NR: AMER-inoculated mice had a weaker shift toward a CRON-associated bacterial profile on the CRON diet Griffin et al. (2017)26 22 healthy 50 g/d palm date for 3 wk and 3.9 g maltodextrin and 33.19g dextrose per day for 3 wk with a 2 wk washout period between interventions FISH Change in gut microbiota composition R: Lower baseline Bacteroides concentration NR: Higher baseline Bacteroides concentration R had a higher dietary fiber intake (18.5 g/d) then NR (6 g/d) R: Significant increase in total bacteria, Bacteroides, Lactobacillus/Enterococcus, C.coccoides-E. rectale, Ruminococcus bromii + flavefaciens, and Roseburia + E. rectale groups NR: No change in gut microbiota Eid et al. (2015)27 34 healthy 16 g/d of inulin-type fructan prebiotic for 3 wk 16S rRNA Illumina sequencing Change in gut microbiota composition R: Lower abundance of unknown genus of Lachnospiraceae NR: Higher abundance of unknown genus of Lachnospiraceae R had higher dietary fiber, energy, fat, PUFA, MUFA, CHO, fiber per 1000 kJ, vegetable, fruit, nut, and seed intakes than NR R: Increase in Bifidobacterium and Faecalibacterium, decrease in Coprococcus, Dorea, and Ruminococcus NR: Increase in Bifidobacterium only Healey et al. (2017) (in press)122 Gut microbiota responsiveness Reference Study information Responsiveness definition Observed baseline differences Observed differences in gut microbiota response Participants Intervention Analysis method Gut microbiota composition and function Dietary intake and host characteristics 10 healthy 100 g of cracker containing 33 g of RS for 3 wk: either RS2 Hi-Maize or RS4 Fibersym 454 pyrosequencing, DGGE, and qPCR Magnitude of change in gut microbiota composition Not investigated Not investigated The magnitude of change varied considerably among individuals. None of the community shifts were observed in all participants Martínez et al. (2010)104 18 healthy 4 wk of each of the following: chews with 0 g, 2.5 g, 5 g, and 10 g/d of GOS 454 pyrosequencing Change in major bacterial groups analyzed: Bifidobacterium and Bacteroides No baseline differences in gut microbiota composition between R and NR Not investigated R: Significant change in at least 1 of the major bacterial groups analyzed after the 5 g and/or 10 g/d GOS NR: Bacterial groups analyzed were unaffected by 5 g and 10 g/d GOS Davis et al. (2011)23 14 overweight mena 1 wk M diet, 3 wk high RS, 3 wk high NSP and 3 wk weight loss diet (high protein, low CHO) Pyrosequencing, DGGE, and qPCR Distance between baseline and postintervention samples on a PCoA Differing baseline gut microbiota composition between R and NR; did not elaborate Not investigated R: Postintervention samples a distance away from the baseline sample on a PCoA NR: Postintervention and baseline samples cluster on a PCoA Walker et al. (2011)105 31 healthy Biscuits containing 6.6 g/d FOS and 3.4 g/d guar gum for 21 d FISH Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR. NR had a baseline bifidobacteria ≥9.3 log10 cells/g Not investigated R: Increase in bifidobacteria NR: No change in bifidobacteria Tuohy et al. (2001)24 9 healthy 8 g/d of IN for 2 wk FISH Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Tuohy et al. (2001)106 8 healthy per group Consumed either 2.5 g, 5 g, 7.5 g, or 10 g/d of SC-FOS, SBOS, GOS, or RS for 7 d (16 groups) Culture based Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Bouhnik et al. (2004)107 30 healthy 2 wk 5 g/d of IN, 1 wk washout and 2 wk of 8 g/d of IN FISH Change in bifidobacteria Significantly lower baseline bifidobacteria levels in R compared with NR for 5 and 8 g/d of IN Not investigated R: Increase in bifidobacteria NR: No change in bifidobacteria Kolida et al. (2007)108 39 healthy 5 g/d IN for 4 wk Culture based Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Bouhnik et al. (2007)96 19 healthy For 4 wk of Lactulose 10 g twice a day OR Synergy 1 (FOS: IN) 10 g twice a day qPCR and DGGE Change in bifidobacteria Lower baseline bifidobacteria levels in R compared with NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria de Preter et al. (2007)109 12 healthy 5 g Synergy 1 (FOS: IN) twice a day for 3 wk qPCR Change in bifidobacteria No significant difference in baseline bifidobacteria between R and NR Not investigated R: Higher response in bifidobacteria NR: Lower response in bifidobacteria Fuller et al. (2007)110 19 healthy 10 g/d or 40 g/d of dietary fiber (crossover design) with a 2 wk washout phase 454 pyrosequencing and qPCR Microbial stability R: Low microbial diversity (OTU numbers) NR: High microbial diversity (OTU numbers) NR had a higher diversity of vegetable intake compared with R R: Higher microbial change NR: Higher microbial stability Tap et al. (2015)20 45 overweight and obeseb 6 wk of calorie-restriction, high protein, low glycemic index diet followed by a 6 wk M diet SOLiD sequencing Microbial gene richness stability R: LGR NR: HGR R fewer fruit, vegetables, and fish products and there was a trend toward lower dietary fiber intakes compared with NR R: Significant increase in gene richness; however gene richness was still significantly lower than in NR NR: No change in gene richness Cotillard et al. (2013)111 14 overweight mena 1 wk M diet, 3 wk high RS, and 3 wk high NSP and 3 wk weight loss diet (high protein, low CHO) HITChip and qPCR Distance between baseline and postintervention samples on PCoA R had significantly lower microbial diversity (inverse Simpson index) than NR No difference in habitual diets between R and NR. NSP intake correlated with microbial diversity in R but not NR R: Postintervention samples a distance away from the baseline sample on a PCoA NR: Postintervention and baseline samples cluster on a PCoA Salonen et al. (2014)21 45 overweight and obeseb 6 wk of calorie-restriction, high protein, low glycemic index diet followed by a 6 wk M diet SOLiD sequencing Change in gut microbiota composition Baseline differences in Bifidobacterium adolescentis, Faecalibacterium prausnitzii, and Eubacterium rectale between R and NR groups Not investigated R: Increase in Bacteroides thetaiotaomicron and a decrease in Lactobacillus reuteri and F. prausnitzii NR: Decrease in L. reuteri Shoaie et al. (2015)103 46 healthy 4 wks 25 g/d NSP, 2 wk washout, and 4 wk 25 g/d NSP + 22 g/d RS diet SCFA only: Gas chromatography Increase in butyrate concentration Baseline butyrate concentratio differences; grouped into quartiles NR (highest quartile) had higher BMI, energy, and protein intakes compared with R (lowest quartile). Fiber intake did not differ R: Increase in butyrate concentration NR: Decrease or no change in butyrate concentration McOrist et al. (2011)22 14 healthy women 7 wk of FMP; 2 × 4 oz./d 16S rRNA bacterial gene sequencing Persistence of Lactococcus lactis strains after FMP stopped R:Lactococcus carrier Higher abundance of Barnesiellaceae, Odoribacteraceae, and Clostridiaceae NR: Lactococcus noncarrier. Lower abundance of Barnesiellaceae, Odoribacteraceae, and Clostridiaceae Not investigated R: Persistence of L. lactis (probiotic within FMP) after FMP stopped (n = 5) NR: No L. lactis persistence after FMP stopped (n = 9) Significantly higher weighted UniFrac distance after 1 wk of FMP in R compared with NR Zhang et al. (2016)25 29 healthy 3 × 3 wk interventions with a 1 wk washout phase between each intervention phase. Chocolate chews with 0, 5, or 7.5 g of agave IN 16S rRNA Illumina sequencing Change in Bifidobacterium relative abundance Not investigated Higher dietary fiber intakes were associated with increased butyrate production and a trend toward higher Bifidobacterium R: Significant increase in Bifidobacterium relative abundance NR: No significant increase in Bifidobacterium relative abundance Holscher et al. (2015)100 10 healthy donors + 30 gnotobiotic mice Fecal samples from 5 CRON individuals and 5 AMER individuals. Inoculated gnotobiotic mice were fed a representative CRON or AMER diet 16S rRNA bacterial gene sequencing Shift in dietary pattern associated bacteria R: Higher abundance of Bacteroides species (cellulosilyticus, thetaiotaomicron), Parabacteroides goldsteinii, and Allstipes putredinis NR: Higher abundance of Dorea longicatena, Blautia, Coporoccus comes, and Clostridium clostridioforme CRON donors consumed 42.1% less calories, 48.6% less fat, 33.5% less carbohydrates, 37.6% less total protein, and 60.8% less protein from animal sources compared with AMER donors R: CRON-inoculated mice had a shift toward the CRON or AMER dietary pattern associated bacterial profile to match the diet (CRON or AMER) consumed NR: AMER-inoculated mice had a weaker shift toward a CRON-associated bacterial profile on the CRON diet Griffin et al. (2017)26 22 healthy 50 g/d palm date for 3 wk and 3.9 g maltodextrin and 33.19g dextrose per day for 3 wk with a 2 wk washout period between interventions FISH Change in gut microbiota composition R: Lower baseline Bacteroides concentration NR: Higher baseline Bacteroides concentration R had a higher dietary fiber intake (18.5 g/d) then NR (6 g/d) R: Significant increase in total bacteria, Bacteroides, Lactobacillus/Enterococcus, C.coccoides-E. rectale, Ruminococcus bromii + flavefaciens, and Roseburia + E. rectale groups NR: No change in gut microbiota Eid et al. (2015)27 34 healthy 16 g/d of inulin-type fructan prebiotic for 3 wk 16S rRNA Illumina sequencing Change in gut microbiota composition R: Lower abundance of unknown genus of Lachnospiraceae NR: Higher abundance of unknown genus of Lachnospiraceae R had higher dietary fiber, energy, fat, PUFA, MUFA, CHO, fiber per 1000 kJ, vegetable, fruit, nut, and seed intakes than NR R: Increase in Bifidobacterium and Faecalibacterium, decrease in Coprococcus, Dorea, and Ruminococcus NR: Increase in Bifidobacterium only Healey et al. (2017) (in press)122 Abbreviations: AMER, American diet with no calorie-restriction; BMI, body mass index; CHO, carbohydrate; CRON, calorie-restricted adequate nutrition diet; DGGE, denaturing gradient gel electrophoresis; FOS, fructo-oligosaccharide; FISH, fluorescence in situ hybridization; FMP, fermented milk product; GOS, galacto-oligosaccharide; HGR, high bacterial gene richness; HITChip, human intestinal tract chip; IN, inulin; LGR, low bacterial gene richness; M, maintenance; MUFA, monounsaturated fatty acids; NR, nonresponder; NSP, nonstarch polysaccharide; OUT, operational taxonomic unit; PCoA, principal coordinate analysis; PUFA, polyunsaturated fatty acids; qPCR, quantitative polymerase chain reaction; R, responder; RS, resistant starch; rRNA, ribosomal RNA; SCFA, short-chain fatty acid, SC-FOS, short-chain fructo-oligosaccharide; SBOS, soybean oligosaccharide; SOLiD, sequencing of oligonucleotides by ligation. a Same study cohort. b Same study cohort. As well as being associated with metabolic and immune-mediated inflammatory disease risk,53 microbial diversity and gene richness also appear to be associated with individualized gut microbiota response.20,21,103 A study conducted in 14 overweight men classified participants as responders and nonresponders based on microbial community stability during 3 dietary interventions: resistant starch, nonstarch polysaccharide, and weight loss interventions. Principal coordinate analysis data identified responders as having gut microbiota communities that were unstable and nonresponders as having gut microbiota communities that were more stable in response to the dietary interventions. It was demonstrated that responders had significantly (p < 0.05) lower baseline alpha diversity scores (bacterial diversity within a sample; inverse Simpson index) than nonresponders.21 Tap and colleagues demonstrated in 19 healthy individuals who consumed a low (10 g/d) vs high (40 g/d) dietary fiber intervention, that individuals with higher alpha diversity (species richness) at baseline had gut microbiota that were more resilient to change (Jensen Shannon Distances metrics) during the high dietary fiber intervention phase.20 Microbial gene richness has also been shown to influence gut microbiota responsiveness. Cotillard and colleagues111 conducted a calorie-restriction study in 45 overweight and obese participants and identified that individuals with high bacterial gene richness (HGR) were less likely to experience a change in gene richness but individuals with low bacterial gene richness (LGR) had a significant (p < 0.01) increase in gene richness in response to the dietary intervention. Shoaie and colleagues103 demonstrated, using the same participant cohort as Cotillard and colleagues,111 that significant (p < 0.05) baseline differences in B. adolescentis, F. prausnitzii, and E. rectale existed between the HGR and LGR groups. Moreover, different bacterial taxa responded to the dietary intervention in the HGR group versus the LGR group. The HGR group experienced a significant (p < 0.05) increase in B. thetaiotaomicron and a decrease in Lactobacillus reuteri and F. prausnitzii, whereas the LGR group only experienced a significant (p < 0.05) decrease in L. reuteri in response to the calorie-restriction diet. These studies highlight that greater microbial diversity and gene richness may lead to a gut microbiota profile that is more resilient to dietary change. Additionally, individuals with differing bacterial gene richness appear to have differing baseline gut microbiota communities that respond distinctively to a given dietary intervention. Interindividual variability in gut microbiota response may also be evident in probiotic intervention studies. Zhang and colleagues25 conducted a study to determine whether any of the probiotic strains found in a fermented milk product (FMP) persisted after the probiotic intervention was stopped. It was reported that persistence of Lactococcus lactis (probiotic strain found in the FMP) was reliant upon whether a participant was a Lactococcus carrier or noncarrier during the washout phase. Lactococcus noncarriers appeared to shed L. lactis, whereas Lactococcus carriers appeared to retain L. lactis after FMP administration. Baseline differences in gut microbiota composition existed between carriers and noncarriers, with carriers having a higher abundance of Barnesiellaceae, Odoribacteraceae, and Clostridiaceae than noncarriers. Carriers also experienced a greater change in beta diversity (bacterial diversity between samples; weighted UniFrac distances) due to the probiotic intervention compared with noncarriers, which suggests that the gut microbiota of Lactococcus carriers were more responsive to the probiotic intervention. It is, therefore, possible that Lactococcus carriers harbored endogenous bacteria that were unable to successfully compete with L. lactis and/or had a colonic environment that allowed L. lactis to thrive (ie, optimal pH and substrate availability) after the FMP intervention was ceased. A number of preliminary studies have demonstrated that baseline gut microbiota differences exist between individuals who have gut microbiota that are responsive and individuals who have gut microbiota that are resilient to a given dietary intervention. Very few studies have, however, been conducted with the primary aim of determining what constitutes a resilient gut microbiota profile. It is highly likely that gut microbiota resilience will differ depending on the dietary intervention being studied and host characteristics, such as age, sex, and habitual dietary intakes, of the study cohort. Influence of habitual dietary intakes. The type and amount of fermentable substrates (ie, indigestible carbohydrates or fiber) normally presented to colonic microbiota will vary considerably depending on the host’s habitual dietary intake. Individuals consuming a high-fiber diet will present their gut microbiota with a large range of fermentable substrates available for use as key energy sources. A low-fiber diet deficient in fermentable substrates will deprive the gut microbiota of external energy sources, resulting in reliance on endogenous fermentable substrates such as mucin. Variability in the types and amounts of available fermentable substrates has a major impact on gut microbiota composition and functional capacity. Gut microbiota distinctions exist among individuals with differing habitual dietary intakes.16 It is, therefore, plausible that the responsiveness of the gut microbiota to an increase in fermentable substrates (ie, prebiotic supplementation) may be influenced by an individual’s habitual dietary intake. Figure 1 provides a hypothetical example of the influence of differing habitual dietary fiber intakes on gut microbiota response to a prebiotic intervention. In this example, individuals with low habitual intakes of fiber are expected to have more responsive gut microbiota because the prebiotic supplement increases the amount of fermentable substrates available, leading to a proliferation of previously low abundance bacterial taxa that are well equipped to use fermentable substrates but had been deprived of them. An alternative scenario may be that bacterial taxa metabolically capable of using fermentable substrates are lost in individuals with low habitual dietary fiber intakes due to chronic fermentable carbohydrate deficiency. Therefore, individuals with higher habitual dietary fiber intakes may harbor gut microbiota that are better equipped to use the additional fermentable substrates, leading to a greater microbial response. Figure 1 Open in new tabDownload slide Hypothetical response in relative abundance of a number of gut microbiota genera (represented by vertical bars) to differences in habitual dietary fiber intake (A, C) and the addition of a prebiotic in the presence of the differing habitual dietary fiber intakes (B, D). This figure depicts that the abundance of certain bacterial genera will markedly differ between individuals with low vs high habitual dietary fiber intakes (the cross-hatched bars indicate a significant [p < 0.05] difference in bacterial relative abundance between low and high habitual dietary fiber consumers) (A, C). The figure also illustrates that certain bacterial genera may respond in a distinctive manner to a prebiotic as a result of differing baseline gut microbiota profiles and habitual dietary fiber intakes (B, D). Hypothetically individuals with low habitual dietary fiber intakes (B) may have a higher proportion of gut microbiota genera, which significantly change in relative abundance in response to a prebiotic (the striped bars indicate a significant [p < 0.05] increase in relative abundance from baseline and the dotted bars indicate a significant [p < 0.05] decrease from baseline) when compared with individuals with high habitual dietary fiber intakes (D). Adapted from Flint et al. (2007).112 Figure 1 Open in new tabDownload slide Hypothetical response in relative abundance of a number of gut microbiota genera (represented by vertical bars) to differences in habitual dietary fiber intake (A, C) and the addition of a prebiotic in the presence of the differing habitual dietary fiber intakes (B, D). This figure depicts that the abundance of certain bacterial genera will markedly differ between individuals with low vs high habitual dietary fiber intakes (the cross-hatched bars indicate a significant [p < 0.05] difference in bacterial relative abundance between low and high habitual dietary fiber consumers) (A, C). The figure also illustrates that certain bacterial genera may respond in a distinctive manner to a prebiotic as a result of differing baseline gut microbiota profiles and habitual dietary fiber intakes (B, D). Hypothetically individuals with low habitual dietary fiber intakes (B) may have a higher proportion of gut microbiota genera, which significantly change in relative abundance in response to a prebiotic (the striped bars indicate a significant [p < 0.05] increase in relative abundance from baseline and the dotted bars indicate a significant [p < 0.05] decrease from baseline) when compared with individuals with high habitual dietary fiber intakes (D). Adapted from Flint et al. (2007).112 Very little is known about the influence of habitual dietary intake on gut microbiota response. Preliminary research has shown that habitual diet may influence gut microbiota responsiveness (Table 1). A recent study conducted using germ-free mice colonized with human gut microbiota from donors with 2 varying dietary patterns (typical American style dietary pattern [AMER] or a plant-rich, calorie-restricted diet with optimal nutrient composition [CRON]) demonstrated that mice inoculated with AMER microbiota were less responsive to the plant-based diet compared with mice inoculated with CRON microbiota. When AMER-colonized mice were cohoused with CRON-colonized mice, it appeared that CRON-associated bacteria were exchanged (ie, via coprophagia) between the 2 mouse types because the AMER microbiota had an improved response to the plant-based diet.26 In humans, a 21-day palm date intervention in 22 healthy individuals did not lead to changes in select bacterial taxa.27 Secondary analysis demonstrated that participants with LDF intakes had a significant change in a number of bacterial taxa, including total bacteria (p < 0.01), Lactobacillus/Enterococcus (p < 0.05), and Roseburia + E. rectale (p < 0.01), whereas participants with HDF intakes had no change in bacterial taxa. These results suggest that participants with HDF intakes harbored gut microbiota that were more resilient and participants with LDF intakes harbored gut microbiota that were more responsive to the palm date intervention.27 Holscher and colleagues demonstrated that an agave inulin supplement (7.5 g/d) led to a >  1% increase in Bifidobacterium relative abundance in only 15 of the 29 participants. A trend toward a positive correlation between Bifidobacterium relative abundance and grams of dietary fiber consumed per kilocalorie was observed. Additionally, a positive correlation between butyrate production and total dietary fiber intakes was established. These results suggest that habitual dietary fiber intakes influence butyrate production and bifidogenic response.100 Some studies have shown that individuals with gut microbiota that are more resilient to dietary change have a higher diversity of vegetable intake20 and higher vegetable, fruit, and fish intakes.111 Other studies have found no differences in habitual dietary intake21 or dietary fiber intakes22 between individuals with responsive gut microbiota and individuals with permissive gut microbiota, indicating that inconsistencies in study results exist. Heterogeneous results may exist due to considerable differences in the dietary interventions and dietary assessment methods used and in participant characteristics between studies. Additionally, in these studies only post hoc analysis was conducted to determine whether dietary differences existed between responders and nonresponders. Until recently no studies had been conducted with the primary aim of determining whether habitual dietary intakes influence the responsiveness of the gut microbiota to dietary interventions. To help fill this knowledge gap, a study was conducted that recruited participants with distinctive habitual dietary fiber intakes to determine whether low vs high habitual dietary fiber influenced the response of the gut microbiota to an inulin-type fructan prebiotic (n = 34) (Healey et al. 2017 [in press]122)). It was established that the LDF group had bacterial taxa that were less responsive to dietary change than the HDF group. The only bacterial genus that significantly changed due to the inulin-type fructan prebiotic in the LDF group was Bifidobacterium (p < 0.01), but in the HDF group significant prebiotic-driven changes in Bifidobacterium (p < 0.01), Faecalibacterium (p < 0.05), Coprococcus (p < 0.05), Dorea (p < 0.05), and Ruminococcus (p < 0.05) were observed. These results differ from those of the palm date intervention study previously noted.27 In the case of the palm date intervention study, the LDF group rather than the HDF group appeared to have gut microbiota that were more responsive to the palm date. The dietary fiber intake criteria used to classify participants as LDF or HDF consumers did differ markedly between studies. The average dietary fiber intake of the LDF (6 g/d vs 18 g/d) and HDF (18.5 g/d vs 38.6 g/d) groups were much lower in the palm date intervention study. The discrepancy in responsiveness between the 2 studies may also be related to the different dietary interventions studied. This suggests that a palm date intervention may have more of an influence on the gut microbiota of LDF consumers and an inulin-type fructan intervention may have more of an influence on the gut microbiota of HDF consumers. Additional research is required to further clarify the influence of habitual dietary fiber intake on gut microbiota responsiveness to certain dietary interventions. Host response to dietary interventions Dietary interventions have been shown to elicit differential gut microbiota responses among individuals. A given dietary intervention leads to variable host responses. Although dietary interventions affect host metabolic outcomes in an individualized way, host parameters such as interindividual variability in gut microbiota composition and habitual dietary intakes may prove useful in predicting host responses. Influence of baseline gut microbiota composition. Because gut microbiota structure and function are implicated in disease risk, it is imperative to investigate whether gut microbiota composition is linked to interindividual variability in host responses. Emerging research suggests that an association between baseline gut microbiota composition and host responses does exist (Table 291,111,113–120). A study conducted in 800 healthy individuals demonstrated that glycemic response to a given food is highly variable among individuals. Researchers were able to use baseline gut microbiota composition, blood parameters, anthropometric measurements, and self-reported lifestyle behavior (ie, dietary habits and physical activity levels) data to identify factors associated with individual variability in glycemic response.113 A machine-learning algorithm was devised, based on the factors associated with individual variability, to accurately predict glycemic responses. The algorithm was used in a human intervention study (n = 26) to generate personalized dietary interventions. The algorithm-derived personalized dietary interventions were shown to significantly (p < 0.05) lower glycemic responses and alter gut microbiota composition.113 This study highlights the potential in using host outcome parameters and baseline gut microbiota data to better predict dietary intervention success. A number of other studies have also demonstrated a link between glycemic response and baseline gut microbiota composition. Individuals with higher baseline Prevotella abundance experienced an incremental decrease in blood glucose and insulin area under the curve after 3 days of consuming barley kernel–based bread.114 The improvement in glycemic response was also associated with an increase in Prevotella/Bacteroides ratio. Individuals with lower baseline Prevotella abundance did not experience a host or gut microbiota response after the dietary intervention, suggesting a more resilient host phenotype and gut microbiota profile.114 Table 2 Observed differences in baseline gut microbiota composition and function and/or dietary intake and host characteristics between responders and nonresponders in studies that demonstrated differing host responses to a dietary intervention Host responsiveness Reference Study information Responsiveness definition Observed baseline differences Observed differences in response Participants Intervention Analysis method Gut microbiota composition and function Dietary intake and host characteristics Host clinical outcome Gut microbiota 12 healthy Algorithm-derived personalized diet 16S rRNA bacterial gene sequencing PPGR Used as a factor to help predict glycemic response: Enterobacteriaceae, Proteobacteria, and Actinobacteria Dietary patterns used as a factor to help predict glycemic response R: Significantly higher PPGR during the “bad” diet phase compared to with “good” diet phase NR: No difference in PPGR between the “good” and “bad” diet phases Interindividual variability in gut microbiota responses; did not analyze R and NR separately Zeevi et al. (2015)113 20 healthy 3 d BKB followed by 3 d of WWB 454 pyrosequencing Improved glucose metabolism R: Higher Prevotella abundance NR: Lower Prevotella abundance Not investigated R: Incremental blood glucose area decreased by at least 25%, total AUC decreased, and insulin AUC decreased by at least 15% NR: No difference in glucose and/or insulin response R: Increase in the Prevotella/Bacteroidetes ratio NR: No change in Prevotella/Bacteroidetes ratio Kovatcheva-Datchary et al. (2015)114 7 healthy 1 wk saccharin intervention—15 mg/kg body weight 16S rRNA bacterial gene sequencing Glycemic response PCoA differences between R and NR Not investigated R: Poor glycemic response to saccharin NR: No glycemic response to saccharin R: Pronounced change in gut microbiota composition NR: No change PCoA differences after the intervention between R and NR Suez et al. (2014)115 49 overweight and obesea 6 wk calorie-restriction diet followed by a 6 wk M diet SOLiD high-throughput sequencing and qPCR Metabolic improvements R: High Akkermansia muciniphilia NR: Low A. muciniphilia R: More metabolically healthy NR: Less metabolically healthy No difference in diet quality between R and NR R: Higher Disse index, greater improvement in LDL cholesterol and a continued decrease in waist circumference NR: Less benefit from intervention R: Reduction in A. muciniphilia (abundance still higher than NR group) NR: Minimal change in A. muciniphilia Dao et al. (2016)117 78 overweight and obese Cohort 1 (n = 52): high fiber rye and WG diet and a low fiber refined grain diet. Cohort 2 (n = 13): 8 g of IN and 8 g of FOS. Cohort 3 (n = 13): 3 wk WL diet (high protein, low CHO) HITChip Change in cholesterol R: High or low abundance of Eubacterium ruminantium and Clostridium felsineum. Lower bifidobacteria NR: Average abundance of E. ruminantium and C. felsineum. Higher bifidobacteria Not investigated R: Decrease (39%) or no change (62%) in cholesterol NR: Decrease (24%) or an increase (23%) in cholesterol R: Microbial stability <0.87. Higher response in bifidobacteria NR: Microbial stability >0.92 Lower response in bifidobacteria Korpela et al. (2014)118 49 overweight and obesea 6 wk of calorie-restriction diet followed by a 6 wk M diet qPCR Intervention-driven WL and M R: Higher concentration of Lactobacillus/Leuconostoc/Pediococcus group NR: Lower concentration of Lactobacillus/Leuconostoc/Pediococcus group NR had a higher starch and oil intake and a lower intake of protein compared with R R: 7.6% WL at 6 wk and 10% at 12 wk NR: 4.4% WL at 6 wk and 2.9% weight regain at 12 wk (only a 1.5% WL over the 12 wk) Not investigated Kong et al. (2013)116 28 healthy Three 4-wk whole-grain flake interventions: WGB, BR + WGB, and BR 454 pyrosequencing Immunological response IL-6 Higher abundance of Dialister and a lower abundance of Coriobacteriaceae in R compared with NR Not investigated R: Highest tertile change in IL-6 due to the BR + WGB intervention NR: Lowest tertile change in IL-6 due to the BR + WGB intervention Not investigated Martínez et al. (2013)119 45 overweight and obesea 6 wk of calorie-restriction diet followed by a 6 wk M diet SOLiD sequencing Change in inflammatory markers R: HGR NR: LGR NR less fruit, vegetables, fish products, and trend toward lower dietary fiber compared with R. NR had higher insulin resistance and fasting serum TAGs compared with to R R: More marked improvement in inflammation markers NR: Less pronounced improvements in inflammatory markers R: No change in gene richness NR: Significant increase in gene richness; however gene richness was still significantly lower than in R Cotillard et al. (2013)111 8 children with IBS 1 wk of LFSD 454 pyrosequencing Reduction in pain frequency R: Higher abundance of an OTU from Ruminococcaceae (specifically Sporobacter and Subdoligranulum) NR: Higher abundance of Bacteroidales and an OTU from Ruminococcaceae No difference in dietary intake between R and NR R: ≥50% reduction in abdominal pain frequency and lower hydrogen production NR: <50% reduction in abdominal pain frequency and higher hydrogen production R: Increased abundance of Prevotellaceae and unclassified Coriobacteriaceae and a reduction in Dialister NR: Increased abundance of Actetivibrio cellulolyticus, Bacteroides, and Dialister Chumpitazi et al. (2014)120 33 children with pediatric Rome III–defined IBS 48 h low FODMAP or typical American childhood diet (crossover design) with a 5 d washout phase 454 pyrosequencing Reduction in pain frequency R: Higher abundance of Bacteroides, Ruminococcaceae, Dorea, Faecalibacterium prausnitzii, and cc_115 (family Erysipilotrichaceae). Three enriched CHO metabolism-specific KEGG orthologs at baseline NR: Higher abundance of Turicibacter from the family Turicibacteraceae. No enriched KEGG orthologs No differences in baseline dietary intake (1 × 3-d diet record) between R and NR R: Significant improvement in pain episodes during low FODMAP diet phase NR: No improvement in pain episodes during low FODMAP diet phase Not investigated Chumpitazi et al. (2015)91 34 healthy 16 g/d of inulin-type fructan prebiotic for 3 wk 16S rRNA Illumina sequencing Improvement in appetite ratings R: Lower abundance of unknown genus of Lachnospiraceae NR: Higher abundance of unknown genus of Lachnospiraceae R had higher dietary fiber, energy, fat, PUFA, MUFA, CHO, fiber per 1000 kJ, vegetable, fruit, nut, and seed intakes than NR R: Significant reduction in satisfaction before lunch and hunger after dinner, and a significant increase in fullness and satisfaction after lunch NR: No significant change in appetite ratings R: Increase in Bifidobacterium and Faecalibacterium, decrease in Coprococcus, Dorea, and Ruminococcus NR: Increase in Bifidobacterium only Healey et al. (in press)122 Host responsiveness Reference Study information Responsiveness definition Observed baseline differences Observed differences in response Participants Intervention Analysis method Gut microbiota composition and function Dietary intake and host characteristics Host clinical outcome Gut microbiota 12 healthy Algorithm-derived personalized diet 16S rRNA bacterial gene sequencing PPGR Used as a factor to help predict glycemic response: Enterobacteriaceae, Proteobacteria, and Actinobacteria Dietary patterns used as a factor to help predict glycemic response R: Significantly higher PPGR during the “bad” diet phase compared to with “good” diet phase NR: No difference in PPGR between the “good” and “bad” diet phases Interindividual variability in gut microbiota responses; did not analyze R and NR separately Zeevi et al. (2015)113 20 healthy 3 d BKB followed by 3 d of WWB 454 pyrosequencing Improved glucose metabolism R: Higher Prevotella abundance NR: Lower Prevotella abundance Not investigated R: Incremental blood glucose area decreased by at least 25%, total AUC decreased, and insulin AUC decreased by at least 15% NR: No difference in glucose and/or insulin response R: Increase in the Prevotella/Bacteroidetes ratio NR: No change in Prevotella/Bacteroidetes ratio Kovatcheva-Datchary et al. (2015)114 7 healthy 1 wk saccharin intervention—15 mg/kg body weight 16S rRNA bacterial gene sequencing Glycemic response PCoA differences between R and NR Not investigated R: Poor glycemic response to saccharin NR: No glycemic response to saccharin R: Pronounced change in gut microbiota composition NR: No change PCoA differences after the intervention between R and NR Suez et al. (2014)115 49 overweight and obesea 6 wk calorie-restriction diet followed by a 6 wk M diet SOLiD high-throughput sequencing and qPCR Metabolic improvements R: High Akkermansia muciniphilia NR: Low A. muciniphilia R: More metabolically healthy NR: Less metabolically healthy No difference in diet quality between R and NR R: Higher Disse index, greater improvement in LDL cholesterol and a continued decrease in waist circumference NR: Less benefit from intervention R: Reduction in A. muciniphilia (abundance still higher than NR group) NR: Minimal change in A. muciniphilia Dao et al. (2016)117 78 overweight and obese Cohort 1 (n = 52): high fiber rye and WG diet and a low fiber refined grain diet. Cohort 2 (n = 13): 8 g of IN and 8 g of FOS. Cohort 3 (n = 13): 3 wk WL diet (high protein, low CHO) HITChip Change in cholesterol R: High or low abundance of Eubacterium ruminantium and Clostridium felsineum. Lower bifidobacteria NR: Average abundance of E. ruminantium and C. felsineum. Higher bifidobacteria Not investigated R: Decrease (39%) or no change (62%) in cholesterol NR: Decrease (24%) or an increase (23%) in cholesterol R: Microbial stability <0.87. Higher response in bifidobacteria NR: Microbial stability >0.92 Lower response in bifidobacteria Korpela et al. (2014)118 49 overweight and obesea 6 wk of calorie-restriction diet followed by a 6 wk M diet qPCR Intervention-driven WL and M R: Higher concentration of Lactobacillus/Leuconostoc/Pediococcus group NR: Lower concentration of Lactobacillus/Leuconostoc/Pediococcus group NR had a higher starch and oil intake and a lower intake of protein compared with R R: 7.6% WL at 6 wk and 10% at 12 wk NR: 4.4% WL at 6 wk and 2.9% weight regain at 12 wk (only a 1.5% WL over the 12 wk) Not investigated Kong et al. (2013)116 28 healthy Three 4-wk whole-grain flake interventions: WGB, BR + WGB, and BR 454 pyrosequencing Immunological response IL-6 Higher abundance of Dialister and a lower abundance of Coriobacteriaceae in R compared with NR Not investigated R: Highest tertile change in IL-6 due to the BR + WGB intervention NR: Lowest tertile change in IL-6 due to the BR + WGB intervention Not investigated Martínez et al. (2013)119 45 overweight and obesea 6 wk of calorie-restriction diet followed by a 6 wk M diet SOLiD sequencing Change in inflammatory markers R: HGR NR: LGR NR less fruit, vegetables, fish products, and trend toward lower dietary fiber compared with R. NR had higher insulin resistance and fasting serum TAGs compared with to R R: More marked improvement in inflammation markers NR: Less pronounced improvements in inflammatory markers R: No change in gene richness NR: Significant increase in gene richness; however gene richness was still significantly lower than in R Cotillard et al. (2013)111 8 children with IBS 1 wk of LFSD 454 pyrosequencing Reduction in pain frequency R: Higher abundance of an OTU from Ruminococcaceae (specifically Sporobacter and Subdoligranulum) NR: Higher abundance of Bacteroidales and an OTU from Ruminococcaceae No difference in dietary intake between R and NR R: ≥50% reduction in abdominal pain frequency and lower hydrogen production NR: <50% reduction in abdominal pain frequency and higher hydrogen production R: Increased abundance of Prevotellaceae and unclassified Coriobacteriaceae and a reduction in Dialister NR: Increased abundance of Actetivibrio cellulolyticus, Bacteroides, and Dialister Chumpitazi et al. (2014)120 33 children with pediatric Rome III–defined IBS 48 h low FODMAP or typical American childhood diet (crossover design) with a 5 d washout phase 454 pyrosequencing Reduction in pain frequency R: Higher abundance of Bacteroides, Ruminococcaceae, Dorea, Faecalibacterium prausnitzii, and cc_115 (family Erysipilotrichaceae). Three enriched CHO metabolism-specific KEGG orthologs at baseline NR: Higher abundance of Turicibacter from the family Turicibacteraceae. No enriched KEGG orthologs No differences in baseline dietary intake (1 × 3-d diet record) between R and NR R: Significant improvement in pain episodes during low FODMAP diet phase NR: No improvement in pain episodes during low FODMAP diet phase Not investigated Chumpitazi et al. (2015)91 34 healthy 16 g/d of inulin-type fructan prebiotic for 3 wk 16S rRNA Illumina sequencing Improvement in appetite ratings R: Lower abundance of unknown genus of Lachnospiraceae NR: Higher abundance of unknown genus of Lachnospiraceae R had higher dietary fiber, energy, fat, PUFA, MUFA, CHO, fiber per 1000 kJ, vegetable, fruit, nut, and seed intakes than NR R: Significant reduction in satisfaction before lunch and hunger after dinner, and a significant increase in fullness and satisfaction after lunch NR: No significant change in appetite ratings R: Increase in Bifidobacterium and Faecalibacterium, decrease in Coprococcus, Dorea, and Ruminococcus NR: Increase in Bifidobacterium only Healey et al. (in press)122 Abbreviations: AUC, area under curve; BR, brown rice; BKB, barley kernel–based bread; CHO, carbohydrate; FODMAP, fermentable oligosaccharides disaccharides monosaccharides and polyols; FOS, fructo-oligosaccharide; HITChip, human intestinal tract chip; HGR, high bacterial gene richness; IBS, irritable bowel syndrome; IL-6, interleukin 6; IN, inulin; KEGG, Kyoto Encyclopedia of Genes and Genomes; LDL, low-density lipoprotein; LFSD, low fermentable substrate diet; LGR, low bacterial gene richness; M, maintenance; MUFA, monounsaturated fatty acids; NR, nonresponder; OTU, operational taxonomic unit; PCoA, principal co-ordinate analysis; PPGR, postprandial glycemic response; PUFA, polyunsaturated fatty acids; qPCR, quantitative polymerase chain reaction; R, responder; rRNA, ribosomal RNA; SOLiD, sequencing of oligonucleotides by ligation; TAG, triacylglycerol; WL, weight loss; WG, whole grain; WWB, white wheat flour bread; WGB, whole-grain barley. a Same study cohort. Table 2 Observed differences in baseline gut microbiota composition and function and/or dietary intake and host characteristics between responders and nonresponders in studies that demonstrated differing host responses to a dietary intervention Host responsiveness Reference Study information Responsiveness definition Observed baseline differences Observed differences in response Participants Intervention Analysis method Gut microbiota composition and function Dietary intake and host characteristics Host clinical outcome Gut microbiota 12 healthy Algorithm-derived personalized diet 16S rRNA bacterial gene sequencing PPGR Used as a factor to help predict glycemic response: Enterobacteriaceae, Proteobacteria, and Actinobacteria Dietary patterns used as a factor to help predict glycemic response R: Significantly higher PPGR during the “bad” diet phase compared to with “good” diet phase NR: No difference in PPGR between the “good” and “bad” diet phases Interindividual variability in gut microbiota responses; did not analyze R and NR separately Zeevi et al. (2015)113 20 healthy 3 d BKB followed by 3 d of WWB 454 pyrosequencing Improved glucose metabolism R: Higher Prevotella abundance NR: Lower Prevotella abundance Not investigated R: Incremental blood glucose area decreased by at least 25%, total AUC decreased, and insulin AUC decreased by at least 15% NR: No difference in glucose and/or insulin response R: Increase in the Prevotella/Bacteroidetes ratio NR: No change in Prevotella/Bacteroidetes ratio Kovatcheva-Datchary et al. (2015)114 7 healthy 1 wk saccharin intervention—15 mg/kg body weight 16S rRNA bacterial gene sequencing Glycemic response PCoA differences between R and NR Not investigated R: Poor glycemic response to saccharin NR: No glycemic response to saccharin R: Pronounced change in gut microbiota composition NR: No change PCoA differences after the intervention between R and NR Suez et al. (2014)115 49 overweight and obesea 6 wk calorie-restriction diet followed by a 6 wk M diet SOLiD high-throughput sequencing and qPCR Metabolic improvements R: High Akkermansia muciniphilia NR: Low A. muciniphilia R: More metabolically healthy NR: Less metabolically healthy No difference in diet quality between R and NR R: Higher Disse index, greater improvement in LDL cholesterol and a continued decrease in waist circumference NR: Less benefit from intervention R: Reduction in A. muciniphilia (abundance still higher than NR group) NR: Minimal change in A. muciniphilia Dao et al. (2016)117 78 overweight and obese Cohort 1 (n = 52): high fiber rye and WG diet and a low fiber refined grain diet. Cohort 2 (n = 13): 8 g of IN and 8 g of FOS. Cohort 3 (n = 13): 3 wk WL diet (high protein, low CHO) HITChip Change in cholesterol R: High or low abundance of Eubacterium ruminantium and Clostridium felsineum. Lower bifidobacteria NR: Average abundance of E. ruminantium and C. felsineum. Higher bifidobacteria Not investigated R: Decrease (39%) or no change (62%) in cholesterol NR: Decrease (24%) or an increase (23%) in cholesterol R: Microbial stability <0.87. Higher response in bifidobacteria NR: Microbial stability >0.92 Lower response in bifidobacteria Korpela et al. (2014)118 49 overweight and obesea 6 wk of calorie-restriction diet followed by a 6 wk M diet qPCR Intervention-driven WL and M R: Higher concentration of Lactobacillus/Leuconostoc/Pediococcus group NR: Lower concentration of Lactobacillus/Leuconostoc/Pediococcus group NR had a higher starch and oil intake and a lower intake of protein compared with R R: 7.6% WL at 6 wk and 10% at 12 wk NR: 4.4% WL at 6 wk and 2.9% weight regain at 12 wk (only a 1.5% WL over the 12 wk) Not investigated Kong et al. (2013)116 28 healthy Three 4-wk whole-grain flake interventions: WGB, BR + WGB, and BR 454 pyrosequencing Immunological response IL-6 Higher abundance of Dialister and a lower abundance of Coriobacteriaceae in R compared with NR Not investigated R: Highest tertile change in IL-6 due to the BR + WGB intervention NR: Lowest tertile change in IL-6 due to the BR + WGB intervention Not investigated Martínez et al. (2013)119 45 overweight and obesea 6 wk of calorie-restriction diet followed by a 6 wk M diet SOLiD sequencing Change in inflammatory markers R: HGR NR: LGR NR less fruit, vegetables, fish products, and trend toward lower dietary fiber compared with R. NR had higher insulin resistance and fasting serum TAGs compared with to R R: More marked improvement in inflammation markers NR: Less pronounced improvements in inflammatory markers R: No change in gene richness NR: Significant increase in gene richness; however gene richness was still significantly lower than in R Cotillard et al. (2013)111 8 children with IBS 1 wk of LFSD 454 pyrosequencing Reduction in pain frequency R: Higher abundance of an OTU from Ruminococcaceae (specifically Sporobacter and Subdoligranulum) NR: Higher abundance of Bacteroidales and an OTU from Ruminococcaceae No difference in dietary intake between R and NR R: ≥50% reduction in abdominal pain frequency and lower hydrogen production NR: <50% reduction in abdominal pain frequency and higher hydrogen production R: Increased abundance of Prevotellaceae and unclassified Coriobacteriaceae and a reduction in Dialister NR: Increased abundance of Actetivibrio cellulolyticus, Bacteroides, and Dialister Chumpitazi et al. (2014)120 33 children with pediatric Rome III–defined IBS 48 h low FODMAP or typical American childhood diet (crossover design) with a 5 d washout phase 454 pyrosequencing Reduction in pain frequency R: Higher abundance of Bacteroides, Ruminococcaceae, Dorea, Faecalibacterium prausnitzii, and cc_115 (family Erysipilotrichaceae). Three enriched CHO metabolism-specific KEGG orthologs at baseline NR: Higher abundance of Turicibacter from the family Turicibacteraceae. No enriched KEGG orthologs No differences in baseline dietary intake (1 × 3-d diet record) between R and NR R: Significant improvement in pain episodes during low FODMAP diet phase NR: No improvement in pain episodes during low FODMAP diet phase Not investigated Chumpitazi et al. (2015)91 34 healthy 16 g/d of inulin-type fructan prebiotic for 3 wk 16S rRNA Illumina sequencing Improvement in appetite ratings R: Lower abundance of unknown genus of Lachnospiraceae NR: Higher abundance of unknown genus of Lachnospiraceae R had higher dietary fiber, energy, fat, PUFA, MUFA, CHO, fiber per 1000 kJ, vegetable, fruit, nut, and seed intakes than NR R: Significant reduction in satisfaction before lunch and hunger after dinner, and a significant increase in fullness and satisfaction after lunch NR: No significant change in appetite ratings R: Increase in Bifidobacterium and Faecalibacterium, decrease in Coprococcus, Dorea, and Ruminococcus NR: Increase in Bifidobacterium only Healey et al. (in press)122 Host responsiveness Reference Study information Responsiveness definition Observed baseline differences Observed differences in response Participants Intervention Analysis method Gut microbiota composition and function Dietary intake and host characteristics Host clinical outcome Gut microbiota 12 healthy Algorithm-derived personalized diet 16S rRNA bacterial gene sequencing PPGR Used as a factor to help predict glycemic response: Enterobacteriaceae, Proteobacteria, and Actinobacteria Dietary patterns used as a factor to help predict glycemic response R: Significantly higher PPGR during the “bad” diet phase compared to with “good” diet phase NR: No difference in PPGR between the “good” and “bad” diet phases Interindividual variability in gut microbiota responses; did not analyze R and NR separately Zeevi et al. (2015)113 20 healthy 3 d BKB followed by 3 d of WWB 454 pyrosequencing Improved glucose metabolism R: Higher Prevotella abundance NR: Lower Prevotella abundance Not investigated R: Incremental blood glucose area decreased by at least 25%, total AUC decreased, and insulin AUC decreased by at least 15% NR: No difference in glucose and/or insulin response R: Increase in the Prevotella/Bacteroidetes ratio NR: No change in Prevotella/Bacteroidetes ratio Kovatcheva-Datchary et al. (2015)114 7 healthy 1 wk saccharin intervention—15 mg/kg body weight 16S rRNA bacterial gene sequencing Glycemic response PCoA differences between R and NR Not investigated R: Poor glycemic response to saccharin NR: No glycemic response to saccharin R: Pronounced change in gut microbiota composition NR: No change PCoA differences after the intervention between R and NR Suez et al. (2014)115 49 overweight and obesea 6 wk calorie-restriction diet followed by a 6 wk M diet SOLiD high-throughput sequencing and qPCR Metabolic improvements R: High Akkermansia muciniphilia NR: Low A. muciniphilia R: More metabolically healthy NR: Less metabolically healthy No difference in diet quality between R and NR R: Higher Disse index, greater improvement in LDL cholesterol and a continued decrease in waist circumference NR: Less benefit from intervention R: Reduction in A. muciniphilia (abundance still higher than NR group) NR: Minimal change in A. muciniphilia Dao et al. (2016)117 78 overweight and obese Cohort 1 (n = 52): high fiber rye and WG diet and a low fiber refined grain diet. Cohort 2 (n = 13): 8 g of IN and 8 g of FOS. Cohort 3 (n = 13): 3 wk WL diet (high protein, low CHO) HITChip Change in cholesterol R: High or low abundance of Eubacterium ruminantium and Clostridium felsineum. Lower bifidobacteria NR: Average abundance of E. ruminantium and C. felsineum. Higher bifidobacteria Not investigated R: Decrease (39%) or no change (62%) in cholesterol NR: Decrease (24%) or an increase (23%) in cholesterol R: Microbial stability <0.87. Higher response in bifidobacteria NR: Microbial stability >0.92 Lower response in bifidobacteria Korpela et al. (2014)118 49 overweight and obesea 6 wk of calorie-restriction diet followed by a 6 wk M diet qPCR Intervention-driven WL and M R: Higher concentration of Lactobacillus/Leuconostoc/Pediococcus group NR: Lower concentration of Lactobacillus/Leuconostoc/Pediococcus group NR had a higher starch and oil intake and a lower intake of protein compared with R R: 7.6% WL at 6 wk and 10% at 12 wk NR: 4.4% WL at 6 wk and 2.9% weight regain at 12 wk (only a 1.5% WL over the 12 wk) Not investigated Kong et al. (2013)116 28 healthy Three 4-wk whole-grain flake interventions: WGB, BR + WGB, and BR 454 pyrosequencing Immunological response IL-6 Higher abundance of Dialister and a lower abundance of Coriobacteriaceae in R compared with NR Not investigated R: Highest tertile change in IL-6 due to the BR + WGB intervention NR: Lowest tertile change in IL-6 due to the BR + WGB intervention Not investigated Martínez et al. (2013)119 45 overweight and obesea 6 wk of calorie-restriction diet followed by a 6 wk M diet SOLiD sequencing Change in inflammatory markers R: HGR NR: LGR NR less fruit, vegetables, fish products, and trend toward lower dietary fiber compared with R. NR had higher insulin resistance and fasting serum TAGs compared with to R R: More marked improvement in inflammation markers NR: Less pronounced improvements in inflammatory markers R: No change in gene richness NR: Significant increase in gene richness; however gene richness was still significantly lower than in R Cotillard et al. (2013)111 8 children with IBS 1 wk of LFSD 454 pyrosequencing Reduction in pain frequency R: Higher abundance of an OTU from Ruminococcaceae (specifically Sporobacter and Subdoligranulum) NR: Higher abundance of Bacteroidales and an OTU from Ruminococcaceae No difference in dietary intake between R and NR R: ≥50% reduction in abdominal pain frequency and lower hydrogen production NR: <50% reduction in abdominal pain frequency and higher hydrogen production R: Increased abundance of Prevotellaceae and unclassified Coriobacteriaceae and a reduction in Dialister NR: Increased abundance of Actetivibrio cellulolyticus, Bacteroides, and Dialister Chumpitazi et al. (2014)120 33 children with pediatric Rome III–defined IBS 48 h low FODMAP or typical American childhood diet (crossover design) with a 5 d washout phase 454 pyrosequencing Reduction in pain frequency R: Higher abundance of Bacteroides, Ruminococcaceae, Dorea, Faecalibacterium prausnitzii, and cc_115 (family Erysipilotrichaceae). Three enriched CHO metabolism-specific KEGG orthologs at baseline NR: Higher abundance of Turicibacter from the family Turicibacteraceae. No enriched KEGG orthologs No differences in baseline dietary intake (1 × 3-d diet record) between R and NR R: Significant improvement in pain episodes during low FODMAP diet phase NR: No improvement in pain episodes during low FODMAP diet phase Not investigated Chumpitazi et al. (2015)91 34 healthy 16 g/d of inulin-type fructan prebiotic for 3 wk 16S rRNA Illumina sequencing Improvement in appetite ratings R: Lower abundance of unknown genus of Lachnospiraceae NR: Higher abundance of unknown genus of Lachnospiraceae R had higher dietary fiber, energy, fat, PUFA, MUFA, CHO, fiber per 1000 kJ, vegetable, fruit, nut, and seed intakes than NR R: Significant reduction in satisfaction before lunch and hunger after dinner, and a significant increase in fullness and satisfaction after lunch NR: No significant change in appetite ratings R: Increase in Bifidobacterium and Faecalibacterium, decrease in Coprococcus, Dorea, and Ruminococcus NR: Increase in Bifidobacterium only Healey et al. (in press)122 Abbreviations: AUC, area under curve; BR, brown rice; BKB, barley kernel–based bread; CHO, carbohydrate; FODMAP, fermentable oligosaccharides disaccharides monosaccharides and polyols; FOS, fructo-oligosaccharide; HITChip, human intestinal tract chip; HGR, high bacterial gene richness; IBS, irritable bowel syndrome; IL-6, interleukin 6; IN, inulin; KEGG, Kyoto Encyclopedia of Genes and Genomes; LDL, low-density lipoprotein; LFSD, low fermentable substrate diet; LGR, low bacterial gene richness; M, maintenance; MUFA, monounsaturated fatty acids; NR, nonresponder; OTU, operational taxonomic unit; PCoA, principal co-ordinate analysis; PPGR, postprandial glycemic response; PUFA, polyunsaturated fatty acids; qPCR, quantitative polymerase chain reaction; R, responder; rRNA, ribosomal RNA; SOLiD, sequencing of oligonucleotides by ligation; TAG, triacylglycerol; WL, weight loss; WG, whole grain; WWB, white wheat flour bread; WGB, whole-grain barley. a Same study cohort. Recent research suggests that artificial sweeteners may be detrimental to health. Suez and colleagues115 demonstrated that saccharin led to glucose intolerance in a subset of their participant cohort. Baseline principal coordinate analysis data demonstrated that individuals who had no saccharin-related change in glycemic response (nonresponders) had gut microbiota profiles that clustered together. Individuals who experienced a glycemic response (responders) had gut microbiota profiles that clustered separately from nonresponders, demonstrating that responders and nonresponders had differing gut microbiota compositions at baseline. Saccharin responders also experienced a more pronounced change in gut microbiota composition due to the saccharin intervention compared with nonresponders, suggesting that dysbiosis may have led to the observed saccharin-related glucose intolerance. Other host metabolic outcomes, such as cholesterol and weight reduction, have been associated with baseline gut microbiota composition differences.116–118 Individuals with low baseline A. muciniphila abundance were shown to be less metabolically healthy but more metabolically resilient to a 6-week calorie-restriction diet than individuals with high baseline A. muciniphila abundance.117 In individuals with high baseline A. muciniphila abundance, the calorie-restriction intervention led to a higher Disse index (assesses insulin sensitivity), greater improvements in low-density lipoprotein cholesterol, a continued reduction in waist circumference, and a reduction in A. muciniphila abundance, even after the calorie-restriction diet was discontinued and participants began a weight maintenance diet.117 Using the same participant cohort, Kong and colleagues116 demonstrated that weight loss success after the calorie-restriction diet was dependent on baseline concentrations of the Lactobacillus/Leuconostoc/Pediococcus group. Individuals with lower concentrations of Lactobacillus/Leuconostoc/Pediococcus experienced a 4.4% reduction in total body weight during the calorie-restriction diet phase but regained 2.9% of their total body weight during the 6-week weight maintenance diet phase. Conversely, individuals with higher concentrations of Lactobacillus/Leuconostoc/Pediococcus at baseline had a 7.6% reduction in total body weight during the calorie-restriction diet phase, and by the end of the weight maintenance diet phase had lost an additional 2.4% of their total body weight. Korpela and colleagues118 used 3 previously published dietary intervention cohorts to determine whether a baseline gut microbiota signature was associated with more pronounced host-specific metabolic changes. A very high or very low abundance of Eubacterium ruminantium and Clostridium felsineum was shown to be associated with a more responsive gut microbiota. Interestingly, individuals with more responsive gut microbiota also had greater changes in cholesterol, demonstrating the prognostic value of baseline gut microbiota. A number of studies have revealed that inflammatory responses to dietary change111,119 and low FODMAP diet-related reductions in IBS-associated pain91,120 are also linked to baseline gut microbiota composition, providing further evidence of the role of baseline gut microbiota composition in host response. At present, the efficacy of a dietary intervention in changing gut microbiota composition and host health outcomes is highly individualized and unpredictable. Mounting evidence suggests that microbial-specific biomarkers could be used to predict the individualized success a given dietary intervention may have on host responses. Influence of habitual dietary intake. Recent research has suggested that habitual dietary intake may also be associated with variability in host responses (Table 2). One study in 45 overweight and obese individuals demonstrated that 3 distinctive dietary pattern clusters were linked with distinctions in metabolic and inflammatory variables and microbial gene richness. Cluster 1 was associated with the least healthy dietary pattern, cluster 3 had the healthiest dietary pattern, and cluster 2 had a dietary pattern between clusters 1 and 3. Individuals with the healthiest dietary pattern cluster had lower levels of the inflammatory marker sCD14, higher levels of anti-inflammatory adipose tissue macrophage CD163, and the highest microbial gene richness compared with clusters 1 and 2.121 In the same participant cohort, Cotillard and colleagues111 demonstrated that a calorie-restriction diet led to more pronounced improvements in inflammatory markers in participants with the healthiest dietary pattern. Other research has confirmed that individuals with healthier dietary patterns have higher microbial gene richness.17 Therefore, individuals with higher microbial gene richness may harbor gut microbiota with a larger repertoire of bacterial genes that are metabolically capable of coping with extreme changes in macronutrient intake, leading to a greater potential to influence host outcomes. A number of studies have been unable to demonstrate dietary intake differences between individuals that have and individuals that have not experienced changes in host outcomes after a given dietary intervention.91,117,120 In these studies, participants were not actively recruited based on distinctions in dietary intakes, and dietary assessment methods that assessed only current rather than habitual dietary intakes were used. As previously noted, the study conducted by the authors of this article recruited individuals with distinctive habitual dietary fiber intakes and demonstrated that habitual dietary fiber intakes influence the response of the gut microbiota to an inulin-type prebiotic intervention (Healey et al. 2017 [in press]). In the same participant cohort, an association between habitual dietary fiber intake and host appetite ratings was also demonstrated. In the HDF group, the inulin-type fructan prebiotic led to a significant reduction in satisfaction before lunch (p < 0.05) and in hunger after dinner (p < 0.01) and a significant increase in fullness (p < 0.01) and satisfaction after lunch (p < 0.05). Changes in appetite ratings were not associated with a change in weight or a reduction in caloric intake, but the 3-week prebiotic intervention may not have been long enough to initiate changes in these host outcomes. Conversely, the LDF group did not experience any changes in appetite ratings, weight, or caloric intake after the prebiotic intervention. The HDF group also had gut microbiota that were more responsive to the inulin-type fructan prebiotic than the LDF group. This is the only study in humans that has been conducted with the primary aim of determining whether differing habitual dietary intakes influence the gut microbiota and host response to a dietary intervention. Therefore, it is essential that additional studies be conducted in this area. CONCLUSION Dietary modulation of the gut microbiota to improve human health is an attractive strategy for reducing the increasing prevalence of metabolic and inflammatory-related disease. The multifaceted interactions that exist within a gut microbiota community make it difficult to predict how a specific dietary intervention may influence gut microbiota composition and host outcomes. In addition, the complexity and individuality of gut microbiota and host responsiveness make any progress in this area challenging. Therefore, gaining a better understanding of the factors implicated in interindividual variability in gut microbiota and host responsiveness may help improve dietary intervention success and subsequently enhance human health outcomes. The majority of studies reviewed suggest that baseline gut microbiota composition and habitual dietary intakes are factors implicated in gut microbiota and host responsiveness. A limited number of studies have found no link between responsiveness and baseline gut microbiota composition23,110 or dietary intake.91,117,120 The discrepancies in results among studies may be related to heterogeneity in participant characteristics (ie, age, sex, ethnicity, BMI) and the differing dietary assessment methods, dietary interventions, and gut microbial analysis methods used. Larger, less heterogeneous cohorts and analytical methods should be used in the future to provide further insight into the influence of baseline gut microbiota composition and habitual dietary intake on gut microbiota and host response to dietary interventions. At present, only a small number of studies have been conducted with the primary aim of determining whether differences in baseline gut microbiota composition113,114,117,118,120 and habitual dietary intake26,113,122 can be used to predict likely responses to a dietary intervention. The other studies reviewed relied on post hoc analysis to determine what factors influenced gut microbiota and host response. Therefore, additional studies that primarily aim to determine what factors are implicated in gut microbiota and host responsiveness are urgently required. Preliminary evidence suggests that future studies aiming to modulate the gut microbiota to improve host outcomes should take baseline gut microbiota composition and habitual dietary intakes into consideration either when recruiting participants or when analyzing study data. This will help provide a deeper understanding of the influence of baseline gut microbiota composition and habitual dietary intake on gut microbiota and host responsiveness so that these factors can be controlled for more effectively. This will help determine the true efficacy of a given dietary intervention and provide better consistency of results among studies. Future developments of predictive mathematical models that integrate various gut microbiota, habitual dietary intake, and host physiological and behavioral parameters, as introduced by Zeevi and colleagues,113 have promise in ensuring dietary interventions are tailored to the individual to improve their success. Continuing advancements in -omics technologies and a deeper understanding of gut microbiota and host physiology resilience may help facilitate the advancement of more robust personalized microbiota-targeted dietary approaches in the future. Acknowledgments Author contributions. GRH compiled the literature and wrote the manuscript. RM, CAB, LB and JC were involved in the development and editing of the manuscript. Funding. GRH received a PhD stipend as part of the Foods for Health fund awarded to The New Zealand Institute for Plant & Food Research Limited and other collaborating New Zealand organizations by the Ministry of Business, Innovation, and Employment, New Zealand Government (C11X1312). Declaration of interest. The authors have no relevant interests to declare. References 1 Alwan A , MacLean DR , Riley LM , et al. Monitoring and surveillance of chronic non-communicable diseases: progress and capacity in high-burden countries . 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TI - Interindividual variability in gut microbiota and host response to dietary interventions
JF - Nutrition Reviews
DO - 10.1093/nutrit/nux062
DA - 2017-12-01
UR - https://www.deepdyve.com/lp/oxford-university-press/interindividual-variability-in-gut-microbiota-and-host-response-to-qvZ7i5U3BM
SP - 1059
VL - 75
IS - 12
DP - DeepDyve
ER -