Opinion

How poverty affects diet to shape the microbiota and chronic disease

Published online:

Abstract

Here, we discuss the link between nutrition, non-communicable chronic diseases and socio-economic standing, with a special focus on the microbiota. We provide a theoretical framework and several lines of evidence from both animal and human studies that support the idea that income inequality is an underlying factor for the maladaptive changes seen in the microbiota in certain populations. We propose that this contributes to the health disparities that are seen between lower-income and higher-income populations in high-income countries.

Main

A prominent paradigm that has developed to explain the complexity of human health involves the mammalian gut microbiota — that is, the intricate community of microorganisms that live within the gastrointestinal tract1. In humans, microbial cells are estimated to roughly equal the number of total human cells (3.8 × 1013 to 3.0 × 1013) or to outnumber nucleated human cells (excluding erythrocytes) by approximately 10:1 (Ref. 2). The gut microbiota affects digestion and absorption by breaking down otherwise indigestible carbohydrates3,4,5, and it protects against pathogens by competitive exclusion6,7. Disruptions in the microbiota, which can occur following antibiotic administration or exposure to persistent genetic or environmental stressors or during disease, can affect the long-term health of both the microbiota and the host8. Moreover, we now appreciate that the intestinal microbiota influences not just the health of the gut itself9 but also health in distal tissues, such as the liver10, the joints11 and the brain12. However, what is less often discussed is how these processes are influenced by the social context in which an individual resides.

The composition of the microbiota can be influenced by host genetics13 and early-life imprinting14,15,16,17 (Box 1), but diet has an inescapably important role in this18,19. The past century has seen a dramatic shift from farm- and home-based eating to industrialized food processing20, an increase in convenience foods and snacks21 and a surge in eating restaurant-prepared foods22. In recent decades, our diets have shifted to higher ratios of processed fats and sugars to fresh foods, and these patterns are influenced heavily by socio-economic status23,24. Individuals with greater socio-economic hardship are often forced to weigh their food selection by a 'calories-per-dollar trade-off', in which the most accessible foods become those that offer more energy and satiation for a lower price. In high-income countries, these trade-offs often necessarily include refined carbohydrates and processed oils and exclude nutrient-dense fresh foods25. In low-income and middle-income countries, the diets of higher-income populations are starting to mimic the processed food intake of higher-income countries, while malnutrition continues to impact the maturity of the microbiota in lower-income populations. These nutritional and epidemiological transitions are a contributing factor that results in a double burden of malnutrition in low-income and middle-income countries, where changes in the food system and increased availability of processed foods has caused these foods to make their way into the diets of the higher-income populations within these countries26,27,28,29.

Box 1: Impact of the microbiota during development

It has been shown that birth method can have a profound effect on the composition of the neonatal microbiota. Babies that are born by caesarian section are more likely to have a gut microbiota dominated by microorganisms from the maternal skin microbiota, such as Staphylococcus spp., whereas vaginally born children have more prevalent colonies of lactose-fermenting Lactobacillus, as well as Prevotella and Sneathia, which are commonly observed in the vagina14. The placental microbiota may also influence a neonate's microbiota or immune status before birth138. Gomez de Aguero et al.139 demonstrated that even a transient microbial exposure in a previously germ-free pregnant mouse can have a lasting impact on the immune system of her offspring, adding to the evidence that the maternal microbial environment in utero can dramatically shape the immune system of children.

Multiple factors, including the duration and exclusivity of breastfeeding, the frequency of bathing, the environment within the home, older siblings or the lack thereof, the presence of pets and interaction with non-familial people, create a unique imprint upon the child's early-life microbiota that may have important health implications. However, more studies are needed to fully understand the effects of each of these specific exposures.

In this Opinion article, we suggest that socio-economically driven nutritional differences affect the microbiota and, consequently, the immune system and host health30,31,32,33,34 (Fig. 1). We discuss this idea using data obtained from both human and mouse models of chronic metabolic disease, with a focus on high-income countries. With chronic preventable diseases on the rise, especially among those least able to afford health care, the pandemic of chronic metabolic diseases affecting the poor becomes impossible to ignore.

Figure 1: Proposed framework for interplay between socio-economic status, the microbiota and metabolic diseases in high-income countries.
Figure 1

In high-income countries (HIC), individuals with low socio-economic status (SES) are presented with several significant complications that may both directly and indirectly influence the composition of the microbiota and, concurrently, the immune system. Specifically, we suggest that environmental stressors, the pressure to eat affordable food that is filling and palatable, along with alterations in health care and medication use, can hamper microbiota diversity and promote a low-grade inflammatory state that precipitates metabolic disease. NCD, non-communicable disease; T2D, type 2 diabetes.

Figure 2: Example of how income influences health through the physiological effects of fibre.
Figure 2

Dietary fibre is a key example of how the microbiota translates dietary input into a physiological and immunological output. Indigestible carbohydrates from the host diet are metabolized by certain families of intestinal microorganisms into short-chain fatty acids (SCFAs), which facilitate colonic homeostasis and immunological tolerance. Fibre is conspicuously missing from the Western diet, and fibre intake is even lower among deprived populations within high-income countries. See also Box 2. NF-κB, nuclear factor κB.

Socio-economic status and chronic disease

Chronic non-communicable diseases have risen in recent decades at a pace too fast to be accounted for by genetics alone35. In 2013, death estimates and disability-adjusted life years suggested a global predominance of non-communicable causes, such as cardiovascular disease, neoplasms and respiratory, metabolic and neurological disease, many of which have a greater incidence since 2000 (Ref. 36). Low socio-economic status is a risk factor for many of these conditions37. Early associations with socio-economic status and health were observed between income level, income instability and mortality, with low income being strongly predictive of mortality independent of educational status or initial health status38. It was reported that raising the minimum wage by a single dollar predicts a 2% decrease in low-birthweight babies and up to a 4% drop in infant death39. Children living in neighbourhoods associated with greater levels of poverty or overcrowding were three times more likely to be hospitalized for influenza than children from wealthier, less crowded neighbourhoods40. These and numerous other reports have shown that societal standing and income status influence susceptibility to many distinct diseases, as we discuss below.

Obesity and metabolic syndrome. In low-income and middle-income countries, obesity is generally more prevalent in individuals of higher incomes41,42,43. However, in high-income settings like Western Europe and the United States, obesity is more prevalent among people with lower incomes44. Of the risk factors for obesity and metabolic syndrome in this setting, food processing and availability appear to be some of the most influential for disease risk41. Low-cost energy-dense foods that are high in fat or sugar content are consistently linked to the prevalence of obesity45, yet these are the most readily available to impoverished populations of wealthier nations. This group further suffers from the loss of protection from nutrient-rich fresh foods, which may be less affordable46. With globalization of the food system, many low-to-medium-income countries, which are increasing their intake of processed and packaged foods47, are also expected to bear a disproportionate burden of metabolic disease incidence in the coming years48. Studies in these regions have shown that dietary and lifestyle changes could significantly prevent onset in these populations48.

Animal studies have highlighted the impact of dietary changes on the microbiota and host health. Germ-free mice are resistant to weight gain in response to a high-fat diet (HFD)49 and have lower metabolic rates than mice maintained under specific-pathogen-free (SPF) conditions3. The introduction of microbiota from SPF mice to germ-free animals induced a 60% increase in body fat, with increased lipogenesis in the liver by facilitating greater monosaccharide absorption from the gut3. In both mice and humans, obesity has been associated with shifts in major phyla of bacteria from a dominance of Bacteroidetes to a dominance of Firmicutes, although this phenomenon is not universally observed, probably owing to regional differences in diet and environment. A cross-sectional study in a German cohort found that obesity was associated with a reduction in Firmicutes50. A study of obese and overweight subjects indicated that at baseline, a lower gut microbial gene count was related to greater insulin resistance, and levels of serum triglycerides and gene diversity increased after a 6-week energy-restricted high-protein dietary intervention with corresponding improvements in insulin resistance but not inflammation in subjects who started with a low gene count51,52. When microbiota from an obese donor is transferred to germ-free recipient mice, the mice gain more weight than recipients of microbiota from lean donors53. Mathematical modelling of the microbiota is quickly expanding to predict diet–microorganism–metabolism contributions to specific metabolites within the gut and is likely to further advance our understanding of the connections between microbiota and metabolism54.

How the microbiota responds or contributes to the obesogenicity of a particular diet is currently under scrutiny. Serino et al.55 suggested that whether or not mice developed diabetes when fed a HFD was related to the nature of their microbiota. Another study indicated that microbial signatures could predispose human subjects to weight-regain after dieting56. Though HFDs can promote changes within the microbiota, the source and nature of the fat are important16,17,30,55,57,58,59 (Table 1). Mice fed a diet based on saturated milk fat developed an expansion of sulfite-reducing bacteria and an exacerbated T helper 1 (TH1) cell immune response, making them susceptible to colitis57. Furthermore, compared with feeding mice a fish oil-based HFD, a lard-based HFD promoted increased weight gain and inflammatory macrophage recruitment to adipose tissue in a Toll-like receptor 4 (TLR4)-dependent manner31. These differential responses to HFDs indicate that the microbiota may determine the distinct metabolic effects that are seen in response to different fat sources.

Table 1: Recent studies investigating connections between dietary fat and the microbiota

Bile acids, which are generated in the liver and secreted into the duodenum following a meal, also modify and are modified by the host microbiota and impact host metabolism. Some microbial families are capable of metabolizing bile acids into downstream signalling molecules, whereas other microbial families are susceptible to the detergent-like properties of bile acids. Thus, fat intake can selectively shape the microbiota through effects on the secretion of bile acids, a topic that has been thoughtfully reviewed elsewhere (Ref. 60).

Food additives, which are ubiquitous among shelf-stable processed foods, may also affect metabolism through effects on the microbiota (Box 2). In one study, even low amounts of dietary emulsifiers — which are extremely common among processed foods — were sufficient to induce changes in the mucus layer of the intestine and provoke low-grade inflammation, making mice susceptible to colitis61. The timing of eating also affects the microbiota, which itself oscillates in composition on a diurnal circadian clock62. Feeding mice a HFD suppressed these oscillations and promoted obesity, whereas time-restricted HFD feeding could partially restore circadian oscillations and attenuate the development of obesity62.

Box 2: The microbiota and host metabolism

The bioavailability of nutrients from the food we consume depends in many ways on the composition of our microbiota140. A fundamental example of this is the fermentation of the non-digestible carbohydrates found in dietary fibre (Fig. 2). Certain families of microorganisms specialize in the fermentation of complex carbohydrates into short-chain fatty acids (SCFAs), specifically butyrate, which has a profound influence on colonic health. Butyrate serves as a primary energy source for colonocytes141 and requires fermentation of fibres by microbial symbionts to become bioavailable to the host. Butyrate stabilizes tight junctions between intestinal epithelial cells142, thereby strengthening the barrier between the host immune cells in the intestinal lamina propria and the commensal microorganisms present in the gut lumen143. Gut permeability and tight-junctional alterations are thought to affect the progression of several diverse diseases. Butyrate also dampens the inflammatory immune response through reprogramming expression of the pro-inflammatory mediator nuclear factor κB (NF-κB)144, blocking differentiation of dendritic cells from stem cell precursors145 and ameliorating colonic inflammation through the promotion of FAS-induced apoptosis in T cells146. Moreover, butyrate promotes the development of regulatory T cells, which protect against the development of colitis147. Diets high in fibre are therefore strongly recommended for reaping these multiplicative health benefits that require partnership with the host microbiota. Adequate amounts of soluble dietary fibre are conspicuously missing from the standard Western diet, especially from processed food.

The microbiota also modulates how efficiently we harvest energy from the foods we consume53. The addition of a conventional microbiota to a previously germ-free mouse promotes glucose uptake and may influence hepatic lipogenesis3, suggesting a potentially critical role for the microbiota in the development of obesity. Indeed, the microbiota has been shown to modulate de novo lipogenesis in the liver3. Moreover, it was reported that the composition of the microbiota can reliably predict the blood glucose response to individual food consumption in a cohort of more than 800 people148. The depletion of certain microorganisms can affect bile acid compositions obtained from the diet149. These bile acids have microbicidal properties that serve to selectively suppress certain populations of bacteria, and this may partly explain the dysbiotic shifts that are observed following the introduction of high-fat diets150. In summary, the microbiota contributes importantly to host metabolism and can be key in translating dietary input into beneficial or harmful health outcomes.

Early disruptions in the establishment of the microbiota may also be obesogenic. In both primates and humans, a maternal HFD during pregnancy has a lasting impact on the microbiota of the offspring even if the mother herself is not obese17. However, leptin, insulin and unfavourable ω-6/ω-3 fatty acid ratios within breast milk were found in mothers with higher body mass index (BMI)63, which could have a lasting impact on the seeding microbiota of the infant gut and immunological training. Administration of antibiotics in the first few years of life has also repeatedly been associated with childhood obesity64,65,66, and maternal antibiotic exposure during pregnancy has been associated with low birthweight, which can be a risk factor for obesity67. Importantly, these disruptions may be more common among low-income populations due to the lower prevalence of breastfeeding68, the misuse of antibiotics and poverty-driven practices such as medication sharing, though more epidemiological studies on the misuse of antibiotics are merited69.

Type 2 diabetes. Between 1980 and 2012, the incidence of type 2 diabetes more than doubled in the United States70, with the alarming appearance of type 2 diabetes among younger age groups71. Alongside obesity, diabetes has been identified as potentially one of the gravest public health epidemics of our time71. On a national level, a 1% increase in gross domestic product (GDP) correlates to a 1.07% rise in diabetes prevalence72. Like obesity, however, type 2 diabetes incidence falls harder on deprived populations in higher-income societies73, even though it has been deemed largely preventable by dietary and lifestyle interventions74. The intractability of diabetes prevalence despite its known preventability suggests an attainability gap in these populations that scientists should not be quick to overlook, especially as we uncover the rippling importance of our diets.

It has been estimated that for every additional 150 kilocalories available per person per day, the world diabetes prevalence increases by 0.1%. However, if those kilocalories happen to come from sugar, prevalence increases by 1.1%, or 11-fold more than for generic calorific consumption72. Furthermore, sugar was the only nutrient to produce a significant correlation to the prevalence of diabetes72, which may not be inherently surprising. Still, sugar-free diet options may not be much better. A recent study showed that non-calorific sweeteners, including the most common diet-soda sweetener aspartame, could still promote glucose intolerance by altering metabolism via effects on the microbiota75. Sucralose (Splenda®) was also included in the study and found to have a similar effect75.

Type 2 diabetes frequently follows a progressive developmental path from insensitivity to insulin to low-grade inflammation and glucose intolerance. It is becoming more evident that just as for obesity, the microbiota is also altered in the progression of diabetes, perhaps as part of the same process. While the obesogenic shifts mentioned earlier are also risk factors for type 2 diabetes, there appear to be some shifts that are diabetes-specific. For instance, in patients with type 2 diabetes, the ratios of bacterial taxa appear to correlate more strongly with blood glucose levels than with BMI, with reductions in the butyrate-producing class Clostridia and expansion of Betaproteobacteria76. One metagenomics study in women with type 2 diabetes found that gene expression patterns in the microbiota could predict a diabetic metabolism, although the markers for European women were region-specific and differed from those for a Chinese population77. Patients with type 2 diabetes display a dysbiosis characterized by an enrichment of sulfate-reducing bacteria and some opportunistic pathogens, and multiple studies have reported a reduction in the frequency of butyrate-producing bacteria76,78. The reported lack of butyrate-producing bacteria could be important in the development of diabetes, as butyrate induces regulatory T cells79 (Box 2), which are known to attenuate inflammation within adipose tissue and reduce insulin resistance in ob/ob mice80.

Cardiovascular disease. Cardiovascular disease (CVD) is another example of a disease affected by socio-economic status. A study among office workers showed that the risk of CVD rose sharply the lower the occupational attainment an individual achieved, with the lowest-level workers surpassing the highest-level workers in CVD risk by a factor of three81. A 2012 study showed that living in a neighbourhood with a low socio-economic profile predisposed healthy Americans to a much greater risk of mortality compared with counterparts in neighbourhoods with higher socio-economic status, independent of their individual socio-economic standing82. An economic downturn during a person's birth years was associated with cardiovascular mortality later in life, suggesting the importance of early years in the development of health disparities83. Higher rates of ischaemic heart disease were found among individuals with low socio-economic status in ten different Western European nations84, and mortality from CVD could be predicted by low socio-economic status among individuals with type 2 diabetes85.

The role of socio-economic status in CVD risk is multifactorial; however, dietary factors and inflammation are two major components. A 15-year follow-up study tracking dietary patterns found that higher dietary quality (for example, more fresh food compared with processed food) could reduce cardiovascular mortality86. It has long been observed that obesity, serum cholesterol and blood pressure serve as major risk factors87, whereas the intake of fruit and vegetables, especially dark leafy greens and cruciferous vegetables, is associated with significant protection across multiple studies88,89. One study found a 27% reduced risk of CVD mortality among individuals who consumed at least three servings of fruit or vegetables per day compared with those who consumed less than one90. The same population was protected against stroke, ischaemic heart disease and all-cause mortality as well.

Several studies have demonstrated a role of the microbiota in mediating dietary promotion of atherosclerosis. For example, the gut microbiota is necessary to metabolize dietary choline, phosphatidylcholine or L-carnitine, to trimethylamine (TMA) and then to the atherogenic compound trimethylamine-N-oxide (TMAO) through the action of the host liver enzyme flavin monooxygenase 3 (FMO3)91,92,93,94. These compounds predicted adverse cardiac events and outcomes in humans and led to the development of atherosclerosis in mice, but this effect required the microbiota93. A HFD in humans has since been shown to elevate post-prandial TMAO levels as well95. Although choline is an important mediator in liver and neuromuscular health96, together, these findings underline an additional effect that poverty and diet may have on the microbiota in modulating metabolic disease risk.

Indeed, as is the case in obesity and diabetes, CVD was found to be accompanied by an altered microbiota and a relative dearth of butyrate-producing microorganisms97, perhaps proceeding from the same root causes. It is important to remember that CVD, obesity and type 2 diabetes can all be thought of as inflammatory conditions that emanate from prolonged subclinical inflammation involving circulating inflammatory mediators98, endothelial vessel inflammation99 and adipose tissue inflammation100. As butyrate serves as a primary anti-inflammatory microbial metabolite (Box 2), its deficiency could potentially contribute to the failure to dampen corrosive systemic inflammation. This is an example of one way in which refined, low-fibre diets leave low-income populations vulnerable to disease.

Socio-economic status and stress

It has been hypothesized that one key component connecting low socio-economic status to chronic disease may be the persistence of stressors in the lives of individuals with low socio-economic status101. The term allostatic load is used to describe the cumulative physiological effects of prolonged stress and is an idea that has persisted for more than 20 years102. Prolonged stress can lead to both accumulated visceral adiposity and greater risk of cardiovascular mortality103. A lower social and financial position in society predisposes individuals to a number of interwoven stressors that are not necessarily experienced as frequently by higher socio-economic status individuals, such as financial stress, shift work, low job control, discrimination, noisier and more crowded living conditions, social stress, neighbourhood crime and reduced access to health care, among others. Social status and social stress have also been repeatedly associated with health outcomes. One prospective cohort study found that receiving support from relatives or a spouse could reduce mortality by 19%, and from a network of regular interaction with six or seven friends by 24%104, indicating that investing in strong relationships may be one of the more beneficial health choices an isolated individual could make. A recent study found an increased mortality from low social integration, but not independently from living in a disadvantaged neighbourhood105, indicating that the stresses that mediate the maladaptive effects of allostatic load may be social and socio-physiological in nature. People receiving low social support have more cardiovascular events and higher death rates as a result of cardiovascular causes, as well as higher all-cause mortality106. And indeed, a study in rhesus macaques demonstrated that disruptions in social status could account for direct effects on the innate and adaptive immune system. Specifically, natural killer cell and TH cell subsets were especially sensitive to changes in gene expression in response to changes in social status, and low-status females tended towards greater inflammatory potential through activation of the MYD88 pathway107.

Sympathetic nerve fibres directly connect to primary and secondary immune organs such as the bone marrow, thymus and spleen, which respectively produce, train and act as hubs of signalling for adaptive immune cells108. Stress is associated with differential cytokine expression, and different kinds of stress may affect the immune system differently. Both chronic and traumatic stress can promote elevated levels of circulating IL-6, a pro-inflammatory cytokine with a complex role in health109,110. Circulating IL-6 is a risk factor for numerous chronic diseases, including cardiovascular and metabolic disorders. It was associated with a 38% risk of heart attack for each elevated quartile of circulating cytokine111, and both C-reactive protein (CRP) and IL-6 have been linked to the development of depression112. Il6-knockout mice are resistant to stress-induced depression113, and certain anti-depressants also have the effect of lowering circulating levels of IL-6 (Ref. 114).

Until recently, connections between neurological function and the microbiota had not been thoroughly investigated, but it is now becoming evident that important connections exist. The vagus nerve connects the brain to the intestines and is capable of communicating shifts in the inflammatory milieu of the gut back to the brain115. In one study, stress from social disruption was found to reduce diversity in the gut microbiota, including a reduction in the relative abundance of the butyrate-producing genera Coprococcus and Dorea116. Similarly, male mice that underwent repeated social defeat in an experimental setting also displayed reduced bacterial diversity in the gut microbiota, which is a feature generally associated with disease117. In a model of early-life stress, maternal stress could induce neural stress pathways through the hypothalamic–pituitary–adrenal axis, but the microbiota was required for the onset of anxiety-like and despairing behaviour, suggesting that disruptions in microbial ecology act as a mechanism for the onset of stress-associated behaviour118. In fact, introduction of a SPF microbiota to germ-free mice could reduce symptoms of anxiety and enhance neurotransmitter turnover119. Though disruption of early-life microbiota through the administration of antibiotics in young rats did not influence anxiety behaviour in maze or open-field tests, it could enhance adult hypersensitivity to visceral pain, suggesting a role for the microbiota in pain sensation120. Another study in humans indicated that probiotics could decrease reported feelings of anxiety in a 30 day period121. Together, these studies indicate that the microbiota is intricately involved in stress, pain sensation and neurological well-being, which deepens its connection to the health outcomes of low-income populations.

Socio-economic status and diet

In a globalized food system, the nutrient quality of food depends on decisions made throughout the food chain122,123. In 2012, three-fourths of the calorific energy purchased among U.S. households came from processed foodstuffs, most of which could be categorized as ready-to-eat20. Processed foods are more likely to be populated with large amounts of fat and sugar while lacking the fibre, vitamin and micronutrient benefits of antioxidant-rich fruits and vegetables45. This has implications for the composition of the microbiota and its effect on the immune system (Box 2).

On the other hand, having the resources to modify diet to increase the well-acknowledged benefits of fresh foods and their accompanying micronutrients may protect against these outcomes. Evidence is accumulating for a role for the microbiota in mediating the effects of diet and disease. Ellagic acid, for instance, which is found in berries, nuts and pomegranates, is emerging as a potent antioxidant that has anti-inflammatory and antitumour immunomodulatory effects124 and that may even mitigate the inflammatory effects of diet-induced obesity and metabolic syndrome through involvement of the microbiota125,126.

Flavonoids, which are metabolized by the microbiota, also have beneficial effects on the immune system. Anthocyanins are phenolic antioxidants of the flavonoid family that are found in deep purple fruits and vegetables, such as blueberries, and have potent anti-inflammatory activity. For example, they inhibit the activation of nuclear factor κB (NF-κB) in monocytes127 and were found to attenuate airway inflammation in a mouse model of asthma128 and to inhibit the growth of cancer cells129. Anthocyanins and other flavones were also associated with reduced levels of insulin resistance, as well as lower levels of CRP130. Importantly, anthocyanins and their metabolic derivatives appear to be necessarily modified by the microbiota131.

Quercetin, a flavonol available in fruits and vegetables, is suggested to regulate hepatic lipid metabolism and prevent adipogenesis and the onset of diet-induced obesity132. Indeed, high doses of quercetin were shown to offset metabolic syndrome133. Quercetin appears to specifically attenuate macrophage expression of pro-inflammatory cytokines, such as tumour necrosis factor (TNF), IL-6 and IL-1β, which are an underlying cause of subclinical inflammation134. It has also been shown to dramatically shift the composition of the gut microbiota towards what is thought to be a more favourable metabolic profile135.

Each of the micronutrients discussed above represents just one of a number of beneficial compounds that are abundant in fresh foods. As such, a diet of fresh food confers numerous microbiota-dependent benefits to the host, but such a diet is often cost-prohibitive and consumed by higher-income families46. These studies highlight the interplay between diet, nutrition, health and the microbiota and indicate the potential negative consequences to those who cannot afford to eat fresh foods regularly.

Despite ongoing debate about the health of certain dietary structures, it is nearly universally recognized that the healthiest diets contain lots of fruits and vegetables, and their protection against premature mortality is well documented136. The key trend through all of this is that some of the most healthy micronutrients that affect the microbiota reside in fresh foods — the same foods that are generally less accessible to the financially challenged25,137. Such nutrients are lost in the energy-dense and nutrient-poor diets that are more frequently consumed by the poor.

Conclusions

Low socio-economic status has repeatedly been listed as a risk factor for numerous metabolic conditions, including obesity, type 2 diabetes and CVD. Although the influences of socio-economic status on health are complex and multifactorial, and although metabolic diseases often include environmental and genetic risk, the contribution of dietary factors is not trivial. In this Opinion, we have considered how the dietary trade-offs faced by individuals who do not consume a beneficial ratio of whole, minimally processed foods (for either financial or social reasons) can have a negative impact on the basic metabolism, immune system and general health of the host, in a manner that is at least partly dependent on the microbiota. We emphasize that this is likely a result of both eating too much of harmful macronutrients, such as high fat, high sugar and refined compounds, and a failure to receive the benefits of fibre fermentation and the immunoregulatory benefits of vitamins and micronutrients. Although we still do not understand all of the mechanisms involved, it is clear that the fibre, vitamins and micronutrients that are found in fresh, whole foods can promote shifts in the microbiota that are associated with protection from the chronic low-level inflammation and immune dysregulation that precipitate the development of inflammatory disease.

Of course, anyone may lose these beneficial effects of the microbiota as a result of personal dietary choices, and there are also social and financial factors that affect dietary choices. Nevertheless, the calories-per-dollar challenge faced by socio-economically challenged individuals in higher-income societies, and the compounding influence of greater stress in their lives, leaves them particularly vulnerable. Studies that address these connections more directly are urgently needed (Box 3). We propose that these relationships should be kept in mind as we move forward in creating meaningful research and a food system and food policies that serve all people.

Box 3: Directions for future research

Although numerous studies have reported the physiological outcomes of socio-economic status and the physiological effects of the microbiota, direct research investigating the social contexts that may impact host–microorganism interactions remains limited. In this Opinion, we have focused largely on potential connections between the microbiota and low socio-economic status among inhabitants of wealthy nations, but there are known differences that are dependent on the national and cultural context as well. For example, in the context of low-income and middle-income countries, the immunological interconnections between socio-economic status, stress, diet and the burden of infectious disease recombine in different ways. Under these circumstances, obesity is no longer an affliction of the poor, but severe malnutrition and its associated effects on the immune system and microbiota are likely to be more prevalent. Moreover, the findings discussed in this article remain largely correlative. Further research that directly investigates the impact of socio-demographic factors on the microbiota and the immune system will greatly enhance our understanding of this area.

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References

  1. 1.

    The human superorganism — of microbes and men. Med. Hypotheses 74, 214–215 (2010).

  2. 2.

    , & Revised estimates for the number of human and bacteria cells in the body. PLoS Biol. 14, e1002533 (2016).

  3. 3.

    et al. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl Acad. Sci. USA 101, 15718–15723 (2004).

  4. 4.

    , & Influence of intestinal microbes on heat production in germ-free, gnotobiotic and conventional mice. J. Nutr. Sci. Vitaminol. 23, 221–226 (1977).

  5. 5.

    et al. Glycan foraging in vivo by an intestine-adapted bacterial symbiont. Science 307, 1955–1959 (2005).

  6. 6.

    & Competitive gut exclusion of avian pathogens by Lactobacillus acidophilus in gnotobiotic chicks. Poult. Sci. 62, 1772–1779 (1983).

  7. 7.

    et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput. Biol. 8, e1002606 (2012).

  8. 8.

    , , , & Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).

  9. 9.

    & The gut microbiota shapes intestinal immune responses during health and disease. Nat. Rev. Immunol. 9, 313–323 (2009).

  10. 10.

    & Gut microbiota and liver diseases. World J. Gastroenterol. 21, 1691–1702 (2015).

  11. 11.

    et al. Molecular insight into gut microbiota and rheumatoid arthritis. Int. J. Mol. Sci. 17, 431 (2016).

  12. 12.

    , & Gut/brain axis and the microbiota. J. Clin. Invest. 125, 926–938 (2015).

  13. 13.

    et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).

  14. 14.

    et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc. Natl Acad. Sci. USA 107, 11971–11975 (2010).

  15. 15.

    , , , & Maternal obesity is associated with alterations in the gut microbiome in toddlers. PLoS ONE 9, e113026 (2014).

  16. 16.

    et al. High-fat maternal diet during pregnancy persistently alters the offspring microbiome in a primate model. Nat. Commun. 5, 3889 (2014).

  17. 17.

    et al. The early infant gut microbiome varies in association with a maternal high-fat diet. Genome Med. 8, 77 (2016).

  18. 18.

    et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

  19. 19.

    et al. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci. Transl Med. 1, 6ra14 (2009).

  20. 20.

    , , & Is the degree of food processing and convenience linked with the nutritional quality of foods purchased by US households? Am. J. Clin. Nutr. 101, 1251–1262 (2015).

  21. 21.

    , & Significant increase in young adults' snacking between 1977–1978 and 1994–1996 represents a cause for concern! Prev. Med. 32, 303–310 (2001).

  22. 22.

    , & Trends in energy intake in U. S. between 1977 and 1996: similar shifts seen across age groups. Obes. Res. 10, 370–378 (2012).

  23. 23.

    et al. Ultra-processed foods and added sugars in the US diet: evidence from a nationally representative cross-sectional study. BMJ Open 6, e009892 (2016).

  24. 24.

    , , & Overweight status and eating patterns among adolescents: where do youths stand in comparison with the healthy people 2010 objectives? Am. J. Publ. Health 92, 844–851 (2011).

  25. 25.

    , & A cost constraint alone has adverse effects on food selection and nutrient density: an analysis of human diets by linear programming. J. Nutr. 132, 3764–3771 (2002).

  26. 26.

    Nutrition transition and the global diabetes epidemic. Curr. Diab. Rep. 15, 64 (2015).

  27. 27.

    & The double burden of malnutrition associated with poverty. Lancet 387, 2504–2505 (2016).

  28. 28.

    et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 382, 427–451 (2013).

  29. 29.

    et al. The association between ownership of common household devices and obesity and diabetes in high, middle and low income countries. CMAJ 186, 258–266 (2014).

  30. 30.

    et al. Effects of diurnal variation of gut microbes and high-fat feeding on host circadian clock function and metabolism. Cell Host Microbe 17, 681–689 (2015).

  31. 31.

    , , , & Bäckhed, F. Crosstalk between gut microbiota and dietary lipids aggravates WAT inflammation through TLR signaling. Cell Metab. 22, 658–668 (2015).

  32. 32.

    , , & Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022–1023 (2006).

  33. 33.

    et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).

  34. 34.

    et al. A top-down systems biology view of microbiome-mammalian metabolic interactions in a mouse model. Mol. Syst. Biol. 3, 112 (2007).

  35. 35.

    The effect of infections on susceptibility to autoimmune and allergic diseases. N. Engl. J. Med. 347, 911–920 (2002).

  36. 36.

    GBD 2013 Risk Factors Collaborators et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 386, 2287–2323 (2015).

  37. 37.

    , , , & Socioeconomic disparities in health in the United States: what the patterns tell us. Am. J. Publ. Health 100, S186–S196 (2010).

  38. 38.

    , , & Income dynamics and adult mortality in the United States, 1972 through 1989. Am. J. Publ. Health 87, 1476–1483 (2011).

  39. 39.

    , , & The effect of an increased minimum wage on infant mortality and birth weight. Am. J. Publ. Health 106, 1514–1516 (2016).

  40. 40.

    & Neighborhood socioeconomic status and influenza hospitalizations among children: New Haven County, Connecticut, 2003–2010. Am. J. Publ. Health 101, 1785–1789 (2012).

  41. 41.

    et al. The global obesity pandemic: shaped by global drivers and local environments. Lancet 378, 804–814 (2011).

  42. 42.

    , & Obesity, SES, and economic development: a test of the reversal hypothesis. Social Sci. Med. 74, 1073–1081 (2012).

  43. 43.

    Cross-national comparison of childhood obesity: the epidemic and the relationship between obesity and socioeconomic status. Int. J. Epidemiol. 30, 1129–1136 (2001).

  44. 44.

    Obesity, diets, and social inequalities. Nutr. Rev. 67 (Suppl. 1), S36–S39 (2009).

  45. 45.

    The real contribution of added sugars and fats to obesity. Epidemiol. Rev. 29, 160–171 (2007).

  46. 46.

    Fat and sugar: an economic analysis. J. Nutr. 133, 838S–840S (2003).

  47. 47.

    , & Overweight exceeds underweight among women in most developing countries. Am. J. Clin. Nutr. 81, 714–721 (2005).

  48. 48.

    in Diabetes Mellitus in Developing Countries and Underserved Communities (ed. Dagogo-Jack, S.) 7–31 (Springer International Publishing, 2017).

  49. 49.

    , , & Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc. Natl Acad. Sci. USA 104, 979–984 (2007).

  50. 50.

    et al. Microbiota and SCFA in lean and overweight healthy subjects. Obesity 18, 190–195 (2010).

  51. 51.

    et al. Dietary intervention impact on gut microbial gene richness. Nature 500, 585–588 (2013).

  52. 52.

    et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).

  53. 53.

    et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).

  54. 54.

    et al. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab. 22, 320–331 (2015).

  55. 55.

    et al. Metabolic adaptation to a high-fat diet is associated with a change in the gut microbiota. Gut 61, 543–553 (2012).

  56. 56.

    et al. Persistent microbiome alterations modulate the rate of post-dieting weight regain. Nature 540, 544–551 (2016).

  57. 57.

    et al. Dietary-fat-induced taurocholic acid promotes pathobiont expansion and colitis in Il10−/− mice. Nature 487, 104–108 (2012).

  58. 58.

    et al. Composition of dietary fat source shapes gut microbiota architecture and alters host inflammatory mediators in mouse adipose tissue. JPEN J. Parenter. Enteral. Nutr. 37, 746–754 (2013).

  59. 59.

    et al. High-fat diet alters gut microbiota physiology in mice. ISME J. 8, 295–308 (2014).

  60. 60.

    , , & Bäckhed, F. Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metab. 24, 41–50 (2016).

  61. 61.

    et al. Dietary emulsifiers impact the mouse gut microbiota promoting colitis and metabolic syndrome. Nature 519, 92–96 (2015).

  62. 62.

    , , & Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 20, 1006–1017 (2014).

  63. 63.

    et al. Human milk leptin, insulin and N6/N3 fatty acids are associated with early differences in gut microbiome of infants born to normal weight and obese mothers. FASEB J. 29 (Suppl. 1), 121.1 (2015).

  64. 64.

    et al. Association of antibiotics in infancy with early childhood obesity. JAMA Pediatr. 168, 1063–1069 (2014).

  65. 65.

    , , , & Antibiotic exposure in infancy and risk of being overweight in the first 24 months of life. Pediatrics 135, 617–626 (2015).

  66. 66.

    & Antibiotics in early life and obesity. Nat. Rev. Endocrinol. 11, 182–190 (2015).

  67. 67.

    et al. Associations between antibiotic exposure during pregnancy, birth weight and aberrant methylation at imprinted genes among offspring. Int. J. Obes. 37, 907–913 (2013).

  68. 68.

    et al. Prevalence of breastfeeding in industrialized countries. Rev. Epidemiol. Sante Publique 60, 305–320 (2012).

  69. 69.

    The role of poverty in antimicrobial resistance. J. Am. Board Fam. Med. 20, 533–539 (2007).

  70. 70.

    et al. Prevalence and incidence trends for diagnosed diabetes among adults aged 20 to 79 years, United States, 1980–2012. JAMA 312, 1218–1226 (2014).

  71. 71.

    & Epidemiology of diabetes — status of a pandemic and issues around metabolic surgery. Diabetes Care 39, 878–883 (2016).

  72. 72.

    , , & The relationship of sugar to population-level diabetes prevalence: an econometric analysis of repeated cross-sectional data. PLoS ONE 8, e57873 (2013).

  73. 73.

    , , , & Diabetes prevalence and socioeconomic status: a population based study showing increased prevalence of type 2 diabetes mellitus in deprived areas. J. Epidemiol. Commun. Health 54, 173–177 (2000).

  74. 74.

    Globalization of diabetes: the role of diet, lifestyle, and genes. 34, 1249–1257 (2011).

  75. 75.

    et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature 514, 181–186 (2014).

  76. 76.

    et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS ONE 5, e9085 (2010).

  77. 77.

    et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).

  78. 78.

    et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).

  79. 79.

    et al. The microbial metabolites, short-chain fatty acids, regulate colonic Treg cell homeostasis. Science 341, 569–573 (2013).

  80. 80.

    et al. Induction of regulatory T cells decreases adipose inflammation and alleviates insulin resistance in ob/ob mice. Proc. Natl Acad. Sci. USA 107, 9765–9770 (2010).

  81. 81.

    , & Inequalities in death — specific explanations of a general pattern? Lancet 323, 1003–1006 (1984).

  82. 82.

    et al. Health status, neighborhood socioeconomic context, and premature mortality in the United States: the National Institutes of Health–AARP Diet and Health Study. Am. J. Public Health 102, 680–688 (2012).

  83. 83.

    , & Being born under adverse economic conditions leads to a higher cardiovascular mortality rate later in life: evidence based on individuals born at different stages of the business cycle. Demography 48, 507–530 (2011).

  84. 84.

    et al. Socioeconomic status and ischaemic heart disease mortality in 10 western European populations during the 1990s. Heart 92, 461–467 (2006).

  85. 85.

    et al. Association between socioeconomic status and mortality, cardiovascular disease, and cancer in patients with type 2 diabetes. JAMA Intern. Med. 176, 1146–1154 (2016).

  86. 86.

    et al. Higher diet quality is associated with decreased risk of all-cause, cardiovascular disease, and cancer mortality among older adults. J. Nutr. 144, 881–889 (2014).

  87. 87.

    , & A general cardiovascular risk profile: the Framingham Study. Am. J. Cardiol. 38, 46–51 (1976).

  88. 88.

    , , & Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies. J. Nutr. 136, 2588–2593 (2006).

  89. 89.

    Agreement on nutrient Intake between the databases of the First National Health and Nutrition Examination Survey and the ESHA Food Processor. Am. J. Epidemiol. 156, 78–85 (2002).

  90. 90.

    et al. Fruit and vegetable intake and risk of cardiovascular disease in US adults: the first National Health and Nutrition Examination Survey Epidemiologic Follow-up Study. Am. J. Clin. Nutr. 76, 93–99 (2002).

  91. 91.

    et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N. Engl. J. Med. 368, 1575–1584 (2013).

  92. 92.

    et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).

  93. 93.

    et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).

  94. 94.

    et al. The TMAO-generating enzyme flavin monooxygenase 3 is a central regulator of cholesterol balance. Cell Rep. 10, 326–338 (2015).

  95. 95.

    et al. Short-term high-fat diet increases postprandial trimethylamine-N-oxide in humans. Nutr. Res. 35, 858–864 (2015).

  96. 96.

    & Thematic Review Series: Glycerolipids. Phosphatidylcholine and choline homeostasis. J. Lipid Res. 49, 1187–1194 (2008).

  97. 97.

    et al. Gut dysbiosis is linked to hypertension. Hypertension 65, 1331–1340 (2015).

  98. 98.

    Inflammation and activated innate immunity in the pathogenesis of type 2 diabetes. Diabetes Care 27, 813–823 (2004).

  99. 99.

    , , & The role of T and B cells in human atherosclerosis and atherothrombosis. Clin. Exp. Immunol. 179, 173–187 (2015).

  100. 100.

    et al. CD8+ effector T cells contribute to macrophage recruitment and adipose tissue inflammation in obesity. Nat. Med. 15, 914–920 (2009).

  101. 101.

    , , & Stress, life events, and socioeconomic disparities in health: results from the Americans' Changing Lives Study. J. Health Soc. Behav. 46, 274–288 (2005).

  102. 102.

    Stress and the individual. Mechanisms leading to disease. Arch. Intern. Med. 153, 2093–2101 (1993).

  103. 103.

    & Stress habituation, body shape and cardiovascular mortality. Neurosci. Biobehav Rev. 56, 139–150 (2015).

  104. 104.

    et al. Influence of the source of social support and size of social network on all-cause mortality. Mayo Clin. Proc. 90, 895–902 (2015).

  105. 105.

    , , , & The joint contribution of neighborhood poverty and social integration to mortality risk in the United States. Ann. Epidemiol. 26, 261–266 (2016).

  106. 106.

    , , , & Montes-Martínez, A. Social network, presence of cardiovascular events and mortality in hypertensive patients. J. Hum. Hypertens. 29, 417–423 (2015).

  107. 107.

    et al. Social status alters immune regulation and response to infection in macaques. Science 354, 1041–1045 (2016).

  108. 108.

    & in Neuroscience: From the Molecular to the Cognitive Vol.100 (ed. Bloom, F.) 157–162 (Elsevier, 1994).

  109. 109.

    et al. Prolonged restraint stress increases IL-6, reduces IL-10, and causes persistent depressive-like behavior that is reversed by recombinant IL-10. PLoS ONE 8, e58488 (2013).

  110. 110.

    et al. Chronic stress, glucocorticoid receptor resistance, inflammation, and disease risk. Proc. Natl Acad. Sci. USA 109, 5995–5999 (2012).

  111. 111.

    , , & Plasma concentration of interleukin-6 and the risk of future myocardial infarction among apparently healthy men. Circulation 101, 1767–1772 (2000).

  112. 112.

    , & Associations of depression with C-reactive protein, IL-1, and IL-6: a meta-analysis. Psychosomat. Med. 71, 171–186 (2009).

  113. 113.

    et al. IL-6 knockout mice exhibit resistance to stress-induced development of depression-like behaviors. Neurobiol. Dis. 23, 587–594 (2006).

  114. 114.

    et al. IL-6 levels decrease with SSRI treatment in patients with major depression. Hum. Psychopharmacol. 20, 473–476 (2005).

  115. 115.

    Physiology and immunology of the cholinergic antiinflammatory pathway. J. Clin. Invest. 117, 289–296 (2007).

  116. 116.

    et al. Exposure to a social stressor alters the structure of the intestinal microbiota: implications for stressor-induced immunomodulation. Brain Behav. Immun. 25, 397–407 (2011).

  117. 117.

    et al. Structural and functional consequences of chronic psychosocial stress on the microbiome and host. Psychoneuroendocrinology 63, 217–227 (2016).

  118. 118.

    et al. Microbiota and host determinants of behavioural phenotype in maternally separated mice. Nat. Commun. 6, 7735 (2015).

  119. 119.

    et al. Commensal microbiota modulate murine behaviors in a strictly contamination-free environment confirmed by culture-based methods. Neurogastroenterol. Motil. 25, 521–528 (2013).

  120. 120.

    et al. Disturbance of the gut microbiota in early-life selectively affects visceral pain in adulthood without impacting cognitive or anxiety-related behaviors in male rats. Neuroscience 277, 885–901 (2014).

  121. 121.

    et al. Beneficial psychological effects of a probiotic formulation (Lactobacillus helveticus R0052 and Bifidobacterium longum R0175) in healthy human volunteers. Gut Microbes 2, 256–261 (2011).

  122. 122.

    , , & Effects of environmental factors and agricultural techniques on antioxidantcontent of tomatoes. J. Sci. Food Agric. 83, 369–382 (2003).

  123. 123.

    Effects of production and processing factors on major fruit and vegetable antioxidants. J. Food Sci. 70, R11–R19 (2005).

  124. 124.

    , , & Ellagic acid modulates the expression of oral innate immune mediators: potential role in mucosal protection. J. Oral Pathol. Med. 44, 214–221 (2015).

  125. 125.

    et al. Polyphenol-rich extract of pomegranate peel alleviates tissue inflammation and hypercholesterolaemia in high-fat diet-induced obese mice: potential implication of the gut microbiota. Br. J. Nutr. 109, 802–809 (2013).

  126. 126.

    , & Ellagic acid attenuates high-carbohydrate, high-fat diet-induced metabolic syndrome in rats. Eur. J. Nutr. 52, 559–568 (2013).

  127. 127.

    et al. Anthocyanins inhibit nuclear factor-kappaB activation in monocytes and reduce plasma concentrations of pro-inflammatory mediators in healthy adults. J. Nutr. 137, 1951–1954 (2007).

  128. 128.

    , , & Anthocyanins inhibit airway inflammation and hyperresponsiveness in a murine asthma model. Food Chem. Toxicol. 45, 1459–1467 (2007).

  129. 129.

    , & Anthocyanin content, antioxidant, anti-inflammatory and anticancer properties of blackberry and raspberry fruits. J. Food Compos. Analysis 23, 554–560 (2010).

  130. 130.

    , , , & Intakes of anthocyanins and flavones are associated with biomarkers of insulin resistance and inflammation in women. J. Nutr. 144, 202–208 (2014).

  131. 131.

    , , & Anthocyanins as inflammatory modulators and the role of the gut microbiota. J. Nutrit. Biochem. 33, 1–7 (2016).

  132. 132.

    , , , & Quercetin reduces high-fat diet-induced fat accumulation in the liver by regulating lipid metabolism genes. Phytother. Res. 27, 139–143 (2013).

  133. 133.

    , , , & Quercetin ameliorates metabolic syndrome and improves the inflammatory status in obese Zucker rats. Obesity 16, 2081–2087 (2008).

  134. 134.

    , & Quercetin attenuates inflammation in human macrophages and adipocytes exposed to macrophage-conditioned media. Int. J. Obes. 35, 1165–1172 (2011).

  135. 135.

    et al. Reshaping faecal gut microbiota composition by the intake of trans-resveratrol and quercetin in high-fat sucrose diet-fed rats. J. Nutrit. Biochem. 26, 651–660 (2015).

  136. 136.

    et al. Fruit and vegetable intake and the risk of cardiovascular disease, total cancer and all-cause mortality — a systematic review and dose-response meta-analysis of prospective studies. Int. J. Epidemiol. 46, 1029–1056 (2017).

  137. 137.

    & Contribution of food prices and diet cost to socioeconomic disparities in diet quality and health: a systematic review and analysis. Nutr. Rev. 73, 643–660 (2015).

  138. 138.

    et al. The placenta harbors a unique microbiome. Sci. Transl Med. 6, 237ra65 (2014).

  139. 139.

    et al. The maternal microbiota drives early postnatal innate immune development. Science 351, 1296–1302 (2016).

  140. 140.

    & The core gut microbiome, energy balance and obesity. J. Physiol. 587, 4153–4158 (2009).

  141. 141.

    Role of anaerobic bacteria in the metabolic welfare of the colonic mucosa in man. Gut 21, 793–798 (1980).

  142. 142.

    , , , & Butyrate enhances the intestinal barrier by facilitating tight junction assembly via activation of AMP-activated protein kinase in Caco-2 cell monolayers. J. Nutr. 139, 1619–1625 (2009).

  143. 143.

    Intestinal mucosal barrier function in health and disease. Nat. Rev. Immunol. 9, 799–809 (2009).

  144. 144.

    , & HDAC inhibition prevents NF-κB activation by suppressing proteasome activity: down-regulation of proteasome subunit expression stabilizes IκBα. Biochem. Pharmacol. 70, 394–406 (2005).

  145. 145.

    et al. Blockade of dendritic cell development by bacterial fermentation products butyrate and propionate through a transporter (Slc5a8)-dependent inhibition of histone deacetylases. J. Biol. Chem. 285, 27601–27608 (2010).

  146. 146.

    et al. Butyrate suppresses colonic inflammation through HDAC1-dependent Fas upregulation and Fas-mediated apoptosis of T cells. Am. J. Physiol. Gastrointest. Liver Physiol. 302, G1405–G1415 (2012).

  147. 147.

    et al. Short-chain fatty acids induce both effector and regulatory T cells by suppression of histone deacetylases and regulation of the mTOR-S6K pathway. Mucosal Immunol. 8, 80–93 (2015).

  148. 148.

    et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).

  149. 149.

    et al. Systemic gut microbial modulation of bile acid metabolism in host tissue compartments. Proc. Natl Acad. Sci. USA 108 (Suppl. 1), 4523–4530 (2011).

  150. 150.

    et al. Bile acid is a host factor that regulates the composition of the cecal microbiota in rats. Gastroenterology 141, 1773–1781 (2011).

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Acknowledgements

Financial support: Dorrance Endowed Fellowship in Pediatric Gastroenterology and Nutrition, University of Arizona Department of Pediatrics

Author information

Affiliations

  1. Christy A. Harrison is at the Departments of Immunobiology and Pediatrics, University of Arizona, Tucson, USA.

    • Christy A. Harrison
  2. Douglas Taren is at the Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, USA.

    • Douglas Taren

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Contributions

The authors contributed equally to researching, writing and editing the review.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Douglas Taren.