. Author manuscript; available in PMC: 2026 Jun 27.

Published in final edited form as: Nat Rev Microbiol. 2023 May 3;21(8):535–550. doi: 10.1038/s41579-023-00888-0

Roles of the gut microbiome in weight management

PMCID: PMC13306846 NIHMSID: NIHMS2140595 PMID: 37138047

Abstract

Overweight, obesity, undernutrition and their respective sequelae exact devastating tolls on personal and public health worldwide. Traditional approaches for treating these conditions with diet, exercise, drugs and/or surgery have met with mixed success, creating an urgent need for new solutions with long-term efficacy. Thanks to transformative advances in sequencing, bioinformatics, and gnotobiotic experimentation, we now understand that the gut microbiome profoundly impacts energy balance through diverse mechanisms affecting both sides of the energy balance equation. Our growing knowledge of microbial contributions to energy metabolism highlights new opportunities for weight management, including the microbiome-aware improvement of existing tools and novel microbiome-targeted therapies. In this Review, we synthesize current knowledge concerning the bidirectional influences between the gut microbiome and existing weight management strategies, including behavior-based and clinical approaches, and incorporate a subject-level meta-analysis contrasting the effects of weight management strategies on microbiota composition. We consider how emerging understanding of the gut microbiome alters our prospects for weight management and the challenges that must be overcome for microbiome-focused solutions to achieve success.

INTRODUCTION

Chronic energy imbalances impact one-third of the global human population. By recent WHO estimates^1,2, 1.9 billion adults and 380 million children worldwide are overweight or obese, with obesity rates tripling since 1975. An additional 462 million adults and 200 million children are undernourished, with undernutrition contributing to 45% of deaths among children under 5. Individuals in developing countries and with low socioeconomic status face disproportionately large burdens of undernutrition plus some of the fastest rising rates of overweight and obesity³. Related morbidities can simultaneously reduce productivity and increase medical expenses, reinforcing links between poverty and poor health. The consequences of energy imbalance are therefore profound and long-lasting for personal and public health, economic development, and social justice.

One reason for the large and growing scale of this problem is that existing treatments have displayed limited long-term success. Overweight and obesity are typically treated with lifestyle interventions that induce negative energy balance by lowering caloric intake and increasing physical activity, with obesity also treated pharmacologically and/or surgically. Such interventions often succeed in the short-term, with most overweight people able to lose >5% of initial weight over a 6-month period⁴. However, there is almost invariably weight regain as acute negative energy balance triggers metabolic adaptations favoring resource sparing and lower resting metabolism^5,6. Undernutrition is typically treated with ready-to-use therapeutic foods (RUTF) that aim to increase caloric and/or protein intake, antibiotics to combat coinfections, and structural changes ameliorating food security. However, these interventions often prove insufficient to rectify undernutrition, particularly in young children. Compounding the problem, undernutrition in early life can alter development that then predisposes individuals to metabolic comorbidities as adults⁷, with some effects even transmissible to subsequent generations⁸.

There remains a pressing need for new approaches to weight management. Recent transformative research has illuminated profound and widespread influences of the gut microbiome on human physiology, including energy balance. The gut microbiome has proven sensitive to existing tools for weight management, including diet and exercise, drugs, and surgical interventions such as gastric bypass. Critically, variations in the gut microbiome can also modulate the efficacy of interventions, suggesting that rational manipulation of the gut microbiome could facilitate weight management. Here, we outline the role of the gut microbiome in energy metabolism, review bidirectional influences between the gut microbiome and existing tools for weight management, and evaluate opportunities and challenges in the development of microbiome-directed therapies targeting energy balance (Fig. 1). While most research to date has focused on gut microbial contributions to overweight and obesity, where possible we draw attention to parallel advances toward elucidating and manipulating the role of the gut microbiome in undernutrition, a promising new frontier for microbiome-targeted medicine.

Figure 1. — Common weight modulating interventions (blue) such as diet, exercise, drugs, and surgery impact gut microbial structure and function, and these changes in the gut microbiome in turn alter intervention efficacy. Gut microbial contributions to weight management are targeted by emerging microbiota-directed therapies (green), including foods engineered to support the engraftment or growth of beneficial microbes, autologous fecal microbiota transplantation after weight loss, and next-generation probiotics.

GUT MICROBIOME AND ENERGY METABOLISM

Germ-free mice reared in sterile conditions have lower adiposity compared with conventionally raised mice or formerly germ-free mice colonized with murine or human gut microbiota^9,10, despite germ-free animals eating more and expending less energy⁹. Similar results have been observed in mice harboring antibiotic-ablated gut microbiomes¹¹. Such studies have illustrated a strong, generally net-positive effect of microbial colonization on host energy status. Moreover, numerous studies have shown that metabolic phenotypes such as obesity, insulin resistance, low-grade inflammation, and/or elevated thermogenesis can be recapitulated in gnotobiotic animals through microbiota transplantation, suggesting causal impacts of the gut microbiome in energy metabolism^12,13. Germ-free animals display physiological and immunological abnormalities versus conventional mice^14,15, including in phenotypes relevant to energy balance like gut barrier function¹⁶, expression of enzymes involved in nutrient acquisition and utilization¹⁷, and gross physiology of the absorptive surface¹⁸. Human-to-mice microbiota transplants also do not perfectly replicate human donor composition¹⁹ and the resource-intensive nature of gnotobiotic animals often leads to underpowered experiments²⁰. Despite these limitations, gnotobiotic animal models remain among our best tools for establishing cause and effect between microbiome composition and host energy balance.

Gnotobiotic studies have enabled us to establish that the gut microbiome causally modulates both sides of the energy balance equation. For instance, the gut microbiome regulates host energy intake via SCFA-mediated hormone secretion as well as direct microbial synthesis of neurotransmitters and hormone mimics that interact with the enteric and central nervous systems to regulate hunger and satiety (Box 1). The gut microbiome increases dietary energy harvest by enhancing small intestinal lipid absorption and salvaging energy from carbohydrates and proteins escaping digestion in the small intestine via fermentation to short-chain fatty acids (SCFA) and other metabolites with residual caloric value (Fig. 2). The gut microbiome directs energy utilization by generating metabolic substrates accessible to select host tissues, regulating bile acid metabolism, and influencing the expression of host genes controlling fatty acid uptake, lipolysis, and thermogenesis (Fig. 3). Finally, the gut microbiome affects interactions between energy balance and inflammation by training systemic immunoreactivity during development, influencing gut barrier integrity, and generating proinflammatory products like lipopolysaccharide (LPS) and flagellin with variable consequences for metabolic health (Box 2). As many of these observations derive from animal research, future studies in humans will be needed to establish translational relevance.

Box 1: Influences of the gut microbiome on energy intake.

Microbial metabolites can alter feeding behavior, as exemplified by the exogenous delivery of SCFA reducing appetite^173,174. These effects are thought to be mediated through the gut-brain axis, a signaling network linking the central and enteric nervous systems. Within the gut, SCFA activate GPR41 and GPR43 receptors on enteroendocrine L-cells, triggering the release of GLP-1 and PYY^175,176. These hormones promote satiety by activating endocrine receptors in the hypothalamus and nucleus of the solitary tract, which process information about nutritional status. SCFA may also affect energy intake by signaling through the vagus nerve¹⁷⁴ or, in the case of acetate, by crossing the blood-brain barrier and inducing the expression of anorexia-promoting genes¹⁷⁷. Many gut bacteria synthesize peptide mimics of hormones regulating satiety and hunger, such as leptin and insulin¹⁷⁸. Gut microbes can also synthesize neurotransmitters such as GABA, dopamine, acetylcholine and norepinephrine¹⁷⁹, as well as affect endogenous levels of serotonin in the intestine¹⁸⁰ and dopamine, norepinephrine and serotonin in the brain¹⁸¹. These neurotransmitters can affect energy intake through their roles in gut motility, satiation and food reward.

Whether the gut microbiome impacts food preferences in humans remains unknown, but such evidence is emerging among animal models. Compared with conventional mice, germ-free mice have higher expression of intestinal receptors for sweet taste and increased sucrose intake¹⁸², as well as increased preference for fat, a result coupled to increased expression of oral fatty acid receptors and decreased expression of satiety peptides¹⁸³. Fecal microbiota transplants from conventional versus diet-induced obese (DIO) mice into germ-free animals were sufficient to transfer the blunted preference for high-fat, high-sugar diets observed among the DIO donors, and was associated with lower markers of food reward¹⁸⁴. Most strikingly, colonization of germ-free mice with gut microbiota from herbivorous, omnivorous or carnivorous wild rodents affected macronutrient intake, with recipients of an herbivorous microbiome selecting diets with higher protein versus carbohydrate¹⁸⁵, potentially because protein is limiting on a plant-based diet. The colonic microbiota of herbivores is also nitrogen-limited, raising the possibility that microbes manipulate host feeding behavior for their own benefit¹⁸⁶. Conversely, fruit flies fed an essential amino acid-deficient diet prefer foods containing microbial taxa capable of ameliorating the deficiency, raising the possibility that hosts also harbor some capacity to select for beneficial microbial functions via diet¹⁸⁷.

Figure 2. — Macronutrients available for breakdown by host enzymes are digested in the small intestine. Small intestinal macronutrient absorption supplies the host with energy predictable by biochemistry (fat: ~9 kcal/g, carbohydrate: ~4 kcal/g, protein: ~4 kcal/g). Dietary fat is readily absorbed in the proximal small intestine, and although fat digestion canonically depends exclusively on host enzymes, evidence of gut microbiome contributions to small intestinal lipid absorption in animal models^152,153 and host-microbial interactions in lipid emulsification¹⁵⁴ challenge this view. By contrast, it is well-accepted that microbes augment carbohydrate and protein digestion. The fractions of carbohydrate and protein digested in the small intestine vary with macronutrient structural form (e.g., higher for sugar versus fiber), meal composition (e.g., higher for fiber-poor versus fiber-rich meals), thermal processing (e.g., higher for cooked foods) and physical processing (e.g., higher for smaller particle sizes)^155,156. Nutrients that escape small intestinal digestion undergo fermentation by the colonic gut microbiota, producing an array of metabolites with energetic implications. The gut microbiome produces branched-chain fatty acids (BCFA) from dietary valine, leucine, and isoleucine, plus other organic acids like lactate and succinate. However, undigested carbohydrates are the principal fuel for microbial fermentation, from which the gut microbiome generates the short-chain fatty acids (SCFA) acetate, butyrate, and propionate. These SCFA are absorbed by the host and contribute to energy metabolism in diverse tissues, with acetate supporting muscle and brain, butyrate supplying up to 60–70% of the energetic needs of the colonic epithelium, and propionate used in hepatic gluconeogenesis¹⁵⁷. Energy returns from SCFA have been estimated at ~1.5 kcal/g¹⁵⁷, less than half the rate for carbohydrates digested in the small intestine. Thus, more energy is harvested by the host when nutrients are digested directly versus fermented. Nevertheless, SCFA account for ~5–10% of daily energy requirements in industrialized populations¹⁵⁸ and almost certainly a greater fraction in populations with fiber-rich diets¹⁵⁷. While SCFA were long appreciated primarily as vehicles for energy salvage, recent research has shown that SCFA possess potent signaling functions that modulate energy intake (gray arrow; see also Box 1), energy utilization (Fig 3), and inflammation (Box 2). These pleiotropic effects help explain why studies of high-fat diets with or without SCFA supplementation have reported inhibitory effects of SCFA on weight gain^159,160. Host metabolites, including bile acids (Fig 3) and immune factors (Box 2), also interact bidirectionally with the gut microbiome and influence its contributions to energy balance.

Figure 3. — Signals from the gut microbiome act on adipocytes to direct whether available calories are allocated to storage or thermogenesis. Early animal experiments showed that gut microbial colonization leads to increased triglyceride storage in adipocytes via intestinal suppression of a circulating lipoprotein lipase inhibitor, fasting-induced adipocyte factor (*Fiaf*), leading to high cellular uptake of fatty acids⁹. SCFA also affect the metabolic state of adipose, intestinal and hepatic tissue. While definitively linking SCFA production to tissue status is challenging due to inconsistent outcomes and designs across studies, intestinal utilization of SCFA by microbes and host, collateral changes in the microbiome, and pleiotropic effects of SCFA on physiology^12,161, many studies have found acetate, butyrate and propionate to exert differential effects. For instance, lipolysis in energy-storing white adipose tissue (WAT) was inhibited and fat accumulation promoted by acetate and propionate^162,163, while butyrate promoted lipolysis and fatty acid oxidation¹⁶⁴. Butyrate enhanced thermogenesis in energy-consuming brown adipose tissue (BAT)¹⁶⁵, while acetate and acetate-rich SCFA mixtures increased WAT browning¹⁶⁰. Propionate promoted gluconeogenesis in the liver¹⁶⁶ and intestine¹⁶⁷, while hepatic acetate has been reported to reduce lipogenesis, limit fat accumulation, increase the expression of thermogenesis and fat oxidation genes, and reduce inflammation^168,169. Moreover, a given SCFA compound can influence physiology in opposing ways in different tissues, as exemplified by acetate or butyrate supplementation increasing GPR43 expression in adipose tissue while reducing it in the colon¹⁶⁰. Such differential effects raise the possibility that metabolic states could be manipulated via delivery of specific SCFA. Gut microbial transformations of bile acids (BA) also regulate energy metabolism. Gut microbes convert host-produced primary BA (e.g., cholic acid and chenodeoxycholic acid) into secondary forms (e.g., deoxycholic acid and lithocholic acid) with diverse signaling functions. For instance, primary and secondary BA are principal ligands for the nuclear farnesoid X receptor (FXR), a transcription factor with key regulatory roles in BA, cholesterol, lipid and glucose metabolism¹⁷⁰. Through FXR signaling, microbial deconjugation of primary BA can decrease hepatic expression of CYP7A1, the rate-limiting enzyme in primary BA synthesis¹⁷¹, with potential downstream effects on lipid absorption in the small intestine¹⁵⁴. Additionally, gut microbial BA metabolism plays a critical role in regulating thermogenesis. Notably, the secondary BA lithocholic acid is a high-affinity agonist of the G protein-coupled receptor TGR5, inducing both WAT and BAT thermogenesis via upregulation of UCP1¹⁷². SCFA: short-chain fatty acid; 1°BA: primary bile acid; 2°BA: secondary bile acid.

Box 2: Interactions of the gut microbiome with low-grade inflammation.

Chronic low-grade inflammation, characterized by aberrant cytokine production and persistent inflammatory signaling, is a central feature in metabolic syndrome. Unlike acute inflammation, which is a response to tissue injury, low-grade inflammation principally arises due to metabolic surplus¹⁸⁸. The gut microbiome plays a key role in modulating low-grade inflammation. Bacterial exposures in the first years of life are critical for establishing systemic immunoreactivity, a topic explored in-depth in recent reviews^189,190. Throughout life, interactions between bacterial products [i.e., microbe-associated molecular patterns (MAMPs)] and innate pattern-recognition receptors expressed the intestinal epithelium, such as Toll-like receptors and NOD-like receptors, activate signaling pathways resulting in immune cell activation and inflammation. Although the mechanistic relationship between inflammation and energy metabolism remains incompletely understood¹⁸⁸, not all microbiota-dependent inflammatory responses have negative consequences for metabolic health. For instance, while exogenous delivery of LPS in mice triggered low-grade systemic inflammation eliciting obesity and insulin resistance¹⁹¹, knockout of flagellin-sensing TLR5 in mice protected against obesity and insulin resistance with protection transmissible to wild-type gnotobiotic mice via microbiota transplantation¹⁹².

Bacteria and proinflammatory bacterial compounds often trigger low-grade inflammation by entering circulation via faults in gut mucosal and epithelial cell barriers, a state colloquially referred to as ‘leaky gut’¹⁹³. Critically, the gut microbiome modulates gut barrier integrity. Gut microbiota conditioned on high-fat diets or dietary emulsifiers can transmit deficient mucosal phenotypes through transplantation, including reduced mucosal barrier thickness, bacterial encroachment toward epithelial cells, and increased translocation of bacterial products into circulation¹⁹⁴. By contrast, many bacterial taxa have been reported to have protective effects on gut barrier integrity. For instance, the mucin-degrading bacterium Akkermansia muciniphila has been shown to promote increased mucus barrier thickness and reduced translocation of LPS into circulation, contributing to reductions in high-fat diet-induced adiposity, adipose tissue inflammation, and insulin resistance¹².

Whether manipulation of energy status by the gut microbiome is beneficial or detrimental depends, of course, on context. Studies at the positive and negative extremes of energetic balance have generally reported that the gut microbiome exacerbates host energetic phenotypes. For instance, the obese-associated microbiome harbors structural and functional changes that increase the capacity for dietary energy harvest²¹. Similar weight potentiating results have been found in dynamic states of positive energy balance, including weight rebound after caloric restriction²², relapsing obesity upon reintroduction of obesogenic conditions after weight cycling²³, and among women in the third trimester of pregnancy²⁴. The gut microbiome can also exacerbate acute states of negative energy balance. For instance, the gut microbiome of children with kwashiorkor, a form of severe acute malnutrition, exhibits a juvenilized state that impairs nutrient uptake^25,26. Likewise, the gut microbiome following Roux-en-Y gastric bypass (RYGB) surgery, a period when weight is shed quickly, has been shown to causally contribute to weight loss²⁷. Similarly, gnotobiotic recipients of microbiomes conditioned on very-low-calorie diets (~800 kcal/day) had decreased adiposity versus recipients of pre-diet microbiomes²⁸. Critically, gnotobiotic transplant recipients of kwashiorkor-associated and post-RYGB-associated microbiomes exhibited lower body mass and adiposity versus germ-free mice^25,27, providing rare examples of microbiomes with net-negative influences on host energy status.

However, the gut microbiome does not always act to exacerbate energy imbalance. Indeed, under at least some conditions, the host-microbial system exhibits a form of dynamic energetic buffering in which short-term reductions in energy uptake by the host foster a microbiome with potentiated contributions to energy status. For instance, in mice fed nutrient-matched raw and cooked diets known to differ in ileal digestibility, the lower digestibility raw diet led to weight loss overall but fostered a gut microbiome that itself promoted increased host energy status, as evidenced by greater weight and adiposity gains among gnotobiotic recipients of microbiomes conditioned on raw diets²⁹. The specific conditions under which the gut microbiome exacerbates versus buffers host energy status remain unclear but constitute a high-priority area for research because such conditions reveal levers for therapeutic manipulation of the gut microbiome.

Another challenge in connecting microbial signatures to metabolic phenotypes is that there could be present consequences of past microbiome states. Studies in mice and humans suggest that disruption of the gut microbiome in early life through pulsed therapeutic or chronic subtherapeutic doses of antibiotics confer increased risks of adult adiposity¹¹. Metabolic consequences of disrupted early-life gut microbiomes were found even when microbiome signatures ultimately rebounded to become indistinguishable from controls³⁰, suggesting that some metabolic contributions of the microbiome will be challenging to track. The proximate mechanisms linking disrupted early-life microbiomes with adult host energetic phenotypes await elucidation. For instance, it remains unknown whether the excess energy that predisposes to obesity comes from increased food intake, reduced activity, reduced resting metabolic rate, or reduced allocation to immunity or reproduction. Similarly, studies in mice and humans suggest that signals of delayed gut microbial maturation can precede the onset of malnutrition in infants³¹. What impairs gut microbial maturation is unknown, but decompartmentalization and small intestinal bacterial overgrowth have been proposed as contributing factors³².

Remarkably, metabolic programing by the gut microbiome may even precede birth, as illustrated by a recent study in mice showing that short-chain fatty acids (SCFA) from the maternal microbiome cross the placental barrier and bind to GPR41 and GPR43 receptors in the developing embryo, affecting downstream tissue development³³. Pups born to mothers harboring microbiomes deficient in SCFA production due to germ-free status, antibiotic treatment or low-fiber diets had higher risks of metabolic syndrome upon encountering high-fat diets as adults than did pups born to mothers harboring SCFA-producing microbiomes. Critically, pups born to both SCFA-deficient and SCFA-producing mothers were surgically delivered and cross-fostered, so this difference was not attributable to vertical inheritance of a SCFA-producing microbiome. Rather, it was the embryonic exposure to SCFA from the maternal microbiome that determined developmental fate and future interaction of metabolic phenotype with diet. Similarly, recent data implicate fetal exposures to microbes³⁴ or vertical inheritance of perturbed maternal microbiomes³⁵ in shaping immune development. Although effects have not yet been investigated in humans, these murine data suggest that the microbiome could be a vehicle for intergenerational modulation of the efficacy of weight management interventions.

Gut microbial influences on energy metabolism can cast shadows over the life course and affect organs far beyond the gut (Fig. 4). The pleiotropy inherent in these diverse mechanisms helps to explain why it can be difficult to predict a priori the effect of gut microbial perturbations on host metabolic responses.

Figure 4. — The gut microbiome contributes to host energy metabolism via early life influences that shape metabolic and immune physiology, as well as dynamic influences throughout life on hunger and satiety, dietary energy harvest, bile acid metabolism, and allocation of available energy to storage versus thermogenesis (Box 1, Figs 2–3). In addition, the gut microbiome modulates interactions between energy metabolism and low-grade inflammation through its calibration of inflammatory tone in early life, production of proinflammatory compounds, and modulation of gut barrier integrity (Box 2). SCFA: short-chain fatty acid, Ac: acetate, Bu: butyrate, Pr: propionate, WAT: white adipose tissue, BAT: brown adipose tissue, LPS: lipopolysaccharide.

DIETS AND WEIGHT MANAGEMENT

Cross-sectional microbiome-wide association studies (MWAS) have provided clear evidence that diet is an important determinant of gut microbiome composition and function^36,37. Large cohort studies have demonstrated links between diet, especially plant-based foods, and microbiome composition and diversity^38,39. Due to the high degree of interindividual variation observed in human microbiome studies, longitudinal analyses of short-term interventions have been particularly powerful in elucidating gut microbial responses to diet. Studies employing this approach have addressed ketogenic and vegetarian diets⁴⁰, high-fat/low-fiber and low-fat/high-fiber diets⁴¹, very-low-calorie diets²⁸ and high-fiber whole-food diets⁴², and have observed diet-induced changes in as little as 1–2 days that were reversible after diet cessation^28,40 (Supplementary Table 1). Such diet-induced plasticity has been linked to success in sustained weight loss interventions⁴³, highlighting the promise of dietary manipulation of host-microbe interactions for weight management.

Dietary properties shaping gut microbial contributions to weight management

Weight management diets frequently manipulate dietary macronutrient content, as exemplified by low-carbohydrate, low-fat or high-protein programs. There may be a false polychotomy between these diets as dietary composition is a zero-sum game, with a reduction in one proportion necessitating another’s rise. For instance, many studies have focused on dietary fat as a driver of microbial outcomes when, in fact, these diets have also differed in sugar and/or fiber content as well as whole-food versus semi-purified states^44,45. Potential pitfalls of focusing on a given macronutrient are highlighted by recent reports that microbial responses to high-fat ketogenic and high-fat non-ketogenic diets are distinct, with ketogenic diets resulting in a loss of bifidobacteria, potentially due to antimicrobial effects of ketones⁴⁶. Moreover, common weight management diets are typically not isocaloric, but drive a reduction in caloric intake that itself can elicit similar clinical outcomes⁴⁷ and microbial responses (Supplementary Table 1).

However, some discrete properties of diet have received attention for their effects on the gut microbiome. Dietary fiber delivers fermentable substrate to the colon, enriching for microbes synthesizing carbohydrate-active enzymes and upregulating SCFA production⁴⁸. Indeed, chronic low-fiber consumption led to extinction of these taxa among mice⁴⁹. In recent human studies, consumption of high-fiber diets increased microbiota-derived glycan-degrading enzymes⁵⁰ or known fermentative taxa⁴², although effects on SCFA production, microbiota diversity and host phenotype varied^42,50. Use of dietary fiber as an adjuvant to pharmacological treatment with acarbose, an α-glucosidase inhibitor used for type 2 diabetes, promoted growth of SCFA-producing microbes and decreased HbA1c levels, a biomarker of blood glucose⁵¹. Yet effects of fiber supplementation have not been uniformly beneficial. For instance, administration of arabinoxylan or long-chain inulin, isolated fibers found in common over-the-counter weight-loss supplements, had differential effects on gut microbial and host phenotypes, with arabinoxylan lowering cholesterol and inulin promoting growth of bifidobacteria, but high inulin doses (30g/day) eliciting inflammation and elevated liver enzymes⁵². Isolated fiber supplements typically target growth of bifidobacteria and lactobacilli⁵³, and therefore may not replicate the effects of a diet rich in diverse polysaccharides. However, because fermentable fibers are differentially capable of stimulating SCFA production⁵⁴, and SCFAs differ in their biological effects on energy metabolism at distal sites (Figs. 3–4, Box 1), precision fiber interventions could be used to engineer the composition of the SCFA pool to alter energy metabolism⁵⁵.

Additives such as emulsifiers and non-caloric artificial sweeteners (NAS) have become ubiquitous in industrialized diets. While generally recognized as safe, recent studies have demonstrated that these compounds impact the gut microbiome and its contributions to energy metabolism^56–58. Emulsifiers like carboxymethylcellulose and polysorbate-80 have been observed to disrupt the intestinal mucosa, leading to microbiota encroachment, low-grade inflammation, adiposity and high blood glucose levels that are transmissible via microbiota transplantation⁵⁸; however, relatively few well-powered studies are available to confirm these findings in humans⁵⁶. Similarly, animal studies have indicated that NAS promote microbiota-transmissible weight gain, metabolic abnormalities and inflammation^57,59–61. However, these results were not corroborated in follow-up animal experiments⁶² or human studies involving saccharin and sucralose^62,63. Such lack of reproducibility between groups and small human study samples sizes (<20 participants/intervention group)^57,61,62 indicate that further investigation and adequately powered double-blind, placebo-controlled studies will be required to establish any deleterious effects of NAS.

Fermented foods and probiotics are frequently suggested as promoting metabolic health, but empirical evidence is limited. Direct comparison of fermented food and probiotic intervention studies is often difficult due to variation in nutrient content and preparation, as well as the varying species and strain compositions used. Diets enriched in various fermented foods – e.g., cheese, kefir, yogurt and kombucha – were recently shown to increase gut microbiota diversity and reduce both pro- and anti-inflammatory cytokines in serum⁵⁰. However, due to the lack of dietary standardization, it remains unclear which functional foods and their respective microbes were responsible for these effects. The immunomodulatory capabilities of conventional lactic acid bacteria-based probiotics are well-documented⁶⁴ and there is emerging evidence for beneficial effects on glucose homeostasis^65,66. However, evidence for their efficacy in weight modulation is limited, with few high-quality studies available^67,68. Yogurt consumption was associated with lower weight, weight gain, BMI, waist circumference and body fat in a systematic review, but cause-effect relationships remain unclear due to confounding variables⁶⁹. Treatment for 12 weeks with Lactobacillus sakei, a probiotic isolated from kimchi, led to reductions in body fat and waist circumference but no effects on body weight or BMI in a recent randomized, double-blind, placebo-controlled study in humans with obesity⁷⁰. Despite robust product marketing, to our knowledge no high-quality evidence currently links kombucha to either immune or metabolic benefits in humans⁷¹.

Specific diets and gut microbial contributions to weight management

Diet is a complex variable with many dimensions affecting microbiome composition, including caloric content, macro- and micronutrients, preparation, and timing of feeding. While each dimension can be studied in isolation in controlled human or animal studies, weight management diets typically incorporate combinations of these variables, making it challenging to decipher their respective contributions. This is further complicated by difficulties inherent in the direct comparison of microbiome data across studies and in comparing microbiome signatures across individuals. To quantify high-level effects of weight management interventions on the human gut microbiome, we used a previously published method⁴⁴ to compare intra-individual microbiome changes in 14 longitudinal studies addressing common weight management interventions including caloric restriction, nutrient modulation, exercise and RYGB surgery (Supplementary Methods). Studies were selected based on design and data availability (Supplementary Table 2). Consistent with previous reports, we found that alpha-diversity was neither a robust nor reproducible indicator of intervention. However, gut microbiota composition was reproducibly altered by interventions across participants in 12 of 14 studies (Fig. 5). An important consideration in interpreting this result is that effects of diet are unlikely to outweigh effects of interindividual variation. While not always reported, we determined that diet explains 0.6–9.0% of variation in gut microbiota composition while interindividual variation explains 55.2–87.0%, depending on study design and distance metric.

Figure 5. — a. Interventions have variable magnitudes of effect on the gut microbiome as measured by the proportion of variation in gut microbiome composition explained within each study, a metric that is robust to how differences in microbiome composition are measured across studies. The panel illustrates results for several of the most common distance metrics used to characterize sample-to-sample differences, including Bray-Curtis, CLR Euclidean, Unweighted UniFrac, and Weighted UniFrac. Differences in the results obtained for different distance metrics reflect their variable treatments of relative abundance and phylogenetic relationships. b. Surgical interventions and dietary interventions tend to display greater effects than exercise in modulating the microbiome, as measured by the proportion of variation explained. The panel illustrates an analysis of Unweighted UniFrac distances, but similar results were observed using other common distance metrics. Caloric modification diets were inclusive of intermittent fasting and diets aiming to reduce caloric intake while nutrient modulation diets involved selective depletion of macronutrients (i.e., low carbohydrate, low-fat, high-protein, ketogenic) and Mediterranean diets. c. To determine similarity in gut microbial response to various diets, a machine learning model was fit to the data. This model demonstrated that high-protein, Mediterranean and low-gluten diets have distinct, readily distinguishable effects on the gut microbiome (indicated by yellow cells). By contrast, the effects of intermittent fasting (IF), ketogenic and vegetarian diets were difficult to separate from general caloric restriction (CR) and the effects of low-carb and low-fat diets were difficult to distinguish from each other (indicated by blue-green cells). d. Analysis of organisms (OTUs) affected by weight management interventions and ordered horizontally by their phylogenetic relatedness revealed conserved effects of diet at the phylum level. Specific phylum-level associations are listed on the right side of the figure. Details of the studies contributing to this meta-analysis are given in Supplementary Table 2 and Supplementary Methods.

Intervention durations varied widely in our dataset, spanning 3 days to 12 weeks. Notably, we did not detect a significant correlation between effect size and intervention duration regardless of distance metric (rho= −0.09–0.2, all P>0.43, Spearman correlation), reinforcing the idea that even short-term dietary interventions can reprogram the gut microbiome^40,72. Interstudy comparison of effect sizes revealed that RYGB exhibited the greatest effect on microbiome composition, followed closely by caloric restriction, nutrient modulation, and lastly exercise (Fig. 5). These classifications are necessarily imperfect as RYGB in part targets caloric restriction, nutrient modulation can affect caloric load, and exercise often alters diet. A sub-analysis of dietary interventions reveals that intermittent fasting, prolonged caloric restriction, and ketogenic diets display the greatest effects on gut microbiota composition, with diets identified as vegetarian or low-gluten having the least effects (Fig. 5). To understand which diets elicit similar effects, we used machine learning methods to predict diet type from diet-induced change in gut microbial composition (Supplementary Methods). We found that the model struggled to differentiate caloric restriction from intermittent fasting, ketogenic, and vegetarian diets, while high-protein, Mediterranean, and low-gluten diets were easily distinguished (Fig. 5). Phylogenetic examination of the organisms responsive to intervention (Fig. 5) demonstrates both high-level signals, such as increases in Proteobacteria after RYGB or in Bacteroides after intermittent fasting or low-carbohydrate diet, but idiosyncrasy within and between diets. While comparisons should be interpreted cautiously as the number of available studies for any given intervention is low, and the nature of sequencing does not capture reductions in absolute abundance as previously identified for very-low-calorie diets²⁸, these observations offer a first glance into the comparative gut microbial effects of weight management interventions.

Diet outcomes

One of the great opportunities in translating microbiome science lies in using the microbiome as a prognostic tool. The high degree of interindividual variation in short- and long-term response to weight loss interventions has been linked to a variety of behavioral and biometric predictors, but the microbiome may offer new biomarkers for intervention efficacy and long-term weight maintenance. Proof-of-principle data in mice demonstrated that classifier models exploiting microbiota composition predicted weight regain in a “yo-yo” dieting paradigm with high accuracy²³. Specific microbial taxa have also been linked to diet efficacy. For instance, baseline levels of Prevotella spp. predicted 6-week weight loss on a high-fiber, whole-grain diet⁷³, and higher gut microbiota diversity and relative abundance of taxa such as Ruminococcaceae and Lachnospiraceae predicted weight gain over a ~10 year period among healthy females from the TwinsUK cohort⁷⁴. In a recent study, it was determined that ~22–38% of change in body fat during weight loss intervention could be explained by baseline microbiota, with certain Clostridia and Parabacteroides spp. indicative of increased loss⁷⁵. Interestingly, elevated baseline microbiota diversity was correlated with lower fat loss, reinforcing the idea that the pursuit of high gut microbiota diversity is unlikely to be uniformly beneficial^76,77. Microbiome composition, when integrated with anthropometric and lifestyle indicators, was also shown to improve the prediction of postprandial glucose response to a given meal, with Proteobacteria linked to poor response⁷⁸. Individuals differ widely in their responses to diet⁷⁹ and determinants of this variation have remained elusive. Such studies offer the tantalizing notion that diets could be tailored to an individual's microbiome to maximize clinical efficacy.

PHYSICAL ACTIVITY AND WEIGHT MANAGEMENT

Like diet, physical activity is a key lever for weight management that impacts the gut microbiome through diverse pathways. Whereas diet can shape the gut microbiome directly by altering the luminal nutritional milieu, effects of physical activity on the gut microbiome are mostly indirect. Correspondingly, effect sizes of physical activity on gut microbiota composition are typically lower than those of diet (Fig 5)^80–82.

Gut microbiota composition is sensitive to both acute and chronic physical activity in rodents^83,84. However, gut microbial signatures of exercise have differed widely across studies, presumably due to differences in exercise type and intensity, diet, species and/or strain, age, and other elements of study design. Even voluntary wheel running versus forced treadmill exercise differentially alter the gut microbiome in mice⁸⁴. Relatively reproducible among effects reported to date is that of voluntary wheel running on the gut microbial capacity for butyrate production, including higher abundances of butyrate-producing taxa and/or increased fecal or cecal concentrations of butyrate, in exercised animals versus sedentary controls⁸⁵.

The gut microbiome also differs between active and sedentary humans^81,86–88. The gut microbiome of human endurance runners and sedentary controls differs at baseline and dynamically changes within individual runners during a distance race^87,88, with enrichments of Veillonella⁸⁷ and Coriobacteriaceae⁸⁸ of particular interest. Similarly, elite rowers⁸⁷ and rugby players^89,90 exhibit distinct gut microbial taxonomic and functional signatures versus sedentary individuals, with modest differences even reported between professional and amateur competitive cyclists⁹¹. Several studies have reported correlations between gut microbiome structure and function and cardiorespiratory fitness, as assessed by oxygen uptake (V̇O2_max or V̇O2_peak)^92,93. In addition, several studies have found increases in SCFA concentrations and/or butyrate-producing taxa in athletes versus sedentary controls⁹⁰ and in individuals with higher versus lower cardiorespiratory fitness⁹³, consistent with data from animal models suggesting that exercise enriches the gut microbiome for SCFA production. A recent study reported that the absolute abundance of SCFA-producing Bacteroides uniformis was correlated with 3,000 m race time and dietary interventions targeting its abundance led to increased performance that could be replicated by administration of B. uniformis to mice⁹⁴. Similarly, subjects with prediabetes and microbiotas with higher basal capacity for SCFA production saw greater improvements in glycemic response after a 12-week high-intensity exercise intervention⁹⁵. Moderate activity also appears to affect gut microbiota composition in similar ways, with active women harboring increased levels of two butyrate producers, Faecalibacterium prausnitzii and Roseburia hominis, and metabolism-modulating Akkermansia muciniphila^96,97 (Box 2).

However, the extent to which these differences are confounded by body composition and other lifestyle factors like diet remain unclear^81,88,98. Indeed, increased daily protein intake of elite rugby players versus sedentary controls accounted for many of the observed intergroup differences in gut microbiota composition⁸⁹. The handful of studies incorporating rigorous dietary control have reported differential and plastic gut microbial responses to physical activity. For instance, among previously sedentary adults exposed to 6 weeks of supervised, endurance exercise with a standardized 3-day dietary intervention implemented before sample collection, changes in the gut microbiome at intervention endpoint differed based on baseline host phenotype⁸⁶. Exercise increased fecal butyrate and acetate concentrations and the relative abundance of butyrate-producing taxa in participants who were lean but not in participants with obesity. Similarly, exercise increased Faecalibacterium in participants who were lean but had opposite effects in participants with obesity. Changes in the microbiome at intervention endpoint were largely reversed following a 6-week sedentary washout period, suggesting that gut microbiome responds dynamically to exercise input.

The mechanisms through which physical activity affects the gut microbiome have not been elucidated, but physical activity alters the luminal environment in diverse ways. For instance, exercise alters cytokine expression in intraepithelial lymphocytes⁹⁹, which play a critical role in mediating host-microbial interactions within the intestinal mucosa¹⁰⁰. Exercise may have hormetic effects on gut barrier integrity, with intensive exercise leading to short-term increased permeability¹⁰¹ but routine exercise promoting reduced permeability, as evidenced by highly trained athletes having lower circulating LPS versus sedentary individuals¹⁰². Among hypercholesterolemic mice, voluntary wheel running increased primary bile acid secretion and fecal excretion¹⁰³, suggesting that exercise may mediate bile acid metabolism. During anaerobic exercise, circulating lactate may translocate into the lumen⁸⁷ and alter luminal pH. Exercise increases reactive oxygen species (ROS) production as well as antioxidant enzyme activity, and studies involving genetic and pharmacological manipulation of ROS have reported impacts on gut microbiota diversity¹⁰⁴. Exercise increases gut motility and reduces colonic transit time¹⁰⁵, which is associated with an altered microbiome composition¹⁰⁶. Physical activity also raises core temperature, induces short-term restrictions to intestinal blood flow¹⁰⁷, alters endocrine signaling¹⁰⁸, generates mechanical forces, and alters food and water consumption in ways expected to influence competitive interactions within the lumen.

Whether these exercise-induced changes in the gut microbiome alter gut microbial contributions to energy metabolism remain unknown. One challenge is the diversity of responses across hosts, such that even studies detecting effects of exercise on the gut microbiome can report no cohort-wide differences in metabolism, possibly due to responder/non-responder effects⁹⁷. The most apparently reproducible effect of exercise on the gut microbiota across human and animal studies, higher butyrate production, might be expected to increase gut barrier integrity and thereby reduce low-grade inflammation, conferring metabolic benefits in cases of overnutrition. It is notable, therefore, that high-fat diet-fed obese mice receiving fecal transplants from normal fat-fed exercised donors exhibited weight loss, lower expression of proinflammatory cytokines in liver, and lower fasting blood glucose levels, in combination with enrichment in butyrate-producing taxa⁸⁰. However, the extent to which these improved metabolic parameters were due to exercise versus diet is uncertain, as the study did not include normal fat-fed non-exercised donor controls and the authors reported that diet had a stronger effect on the gut microbiome than did exercise⁸⁰. Studies isolating the energetic consequences of exercise-induced changes in the gut microbiome are especially needed and will enrich our knowledge of available lifestyle levers for microbiota-directed weight management.

CLINICAL PATHS AND WEIGHT MANAGEMENT

When diet, exercise, and other behavioral interventions prove insufficient, clinical intervention may be required to manage weight and related sequelae. In this section, we discuss interactions between medical interventions and the gut microbiome as well as microbiota-targeted clinical therapies for weight management.

Surgical approaches

Surgical treatments for severe obesity aim to reduce food intake and/or decrease nutrient absorption¹⁰⁹. These interventions have been reproducibly shown to increase microbiota richness as well as increase the relative abundance of Proteobacteria/Enterobacteriaceae and Akkermansia (Supplementary Table 2)^110–112. These intervention-associated changes in microbiota composition have been functionally linked both to shifts in microbial metabolism¹¹² and to host health through the demonstration of reduced adiposity among gnotobiotic recipients of post-intervention versus control gut microbiota¹¹³. Unfortunately, there is evidence that the effects of surgical intervention on the microbiome may be short-lived, with substantial reversion within one year post-intervention¹¹⁴. While surgical approaches such as RYGB tend to be highly effective overall, there is a subset of patients who exhibit poor initial response or will regain weight following their surgery¹¹⁵. Current evidence that the gut microbiome contributes to variable post-RYGB responses is equivocal^116,117, with studies concurring that gut microbiota composition differs only modestly between individuals experiencing successful and poor weight loss outcomes. Nevertheless, fecal transplants from human post-RYGB patients to antibiotic-treated mice demonstrated that recipient weight gain phenotypes tracked donor outcomes¹¹⁷, illustrating that the gut microbiome participates to some extent in weight loss success. Significantly higher levels of Barnesiella spp. were observed among humans experiencing poor weight loss outcomes and their murine gnotobiotic recipients, but further experiments are required to evaluate reproducibility and causal links. Although research is at an early stage, available data suggest that the microbiome can modulate surgical success to some extent, raising an important but unanswered question: Could we mimic the effects of surgery through precision modification of the gut microbiome alone?

Drugs

Interindividual variation in drug response is a major challenge in medicine and is likely mediated in part by the gut microbiome¹¹⁸. There are multiple mechanisms through which this may occur, including direct interactions between gut microbes and oral drugs, off-target antibacterial effects of common drugs on the gut microbiome¹¹⁹, and effects of drugs on host physiological systems interacting with the gut microbiome¹¹⁸. While this nascent field is rapidly expanding, its importance to the treatment of weight management and metabolic health is clear.

Perhaps the best-known example of drug-microbiome interactions in metabolic health involves metformin, a first-line therapy for type 2 diabetes. Remarkably, transplantation of metformin-treated human donor feces into germ-free mice was capable of improving glucose tolerance as compared to pre-treatment donor feces, providing strong evidence that the gut microbiome contributes to drug efficacy¹²⁰. Although metformin is well-tolerated and widely used, approximately 30% of metformin-treated patients will experience gastrointestinal side effects that have been correlated with the intestinal abundance of E. coli¹²¹. Alternatively, the α-glucosidase inhibitor acarbose has considerable off-target effects on the gut microbiota¹²². A highly prevalent microbial acarbose resistance mechanism resulting in drug inactivation has recently been identified, although its significance to drug efficacy remains to be determined in prospective studies¹²³.

Statins are commonly prescribed for the prevention and control of cardiovascular disease and target HMG-CoA reductase, which is involved in cholesterol biosynthesis. Across >3,000 participants in 3 cohorts, statin use was negatively associated with an inflammation-promoting microbial signature characterized by a high proportion of Bacteroides, a low proportion of Faecalibacterium, and low absolute microbial cell density¹²⁴. These findings were recently replicated and expanded to show that microbiome composition is capable of predicting treatment responses, with a Bacteroides-enriched, low-diversity microbiota associated with increased adverse outcomes but also increased therapeutic effects¹²⁵. Lower therapeutic benefits of statins among individuals with higher baseline gut microbial diversity was found even after correcting for the possibility that high diversity individuals are healthier and/or prescribed less-potent statin doses¹²⁵, suggesting that high gut microbial diversity itself may hamper drug efficacy.

Fecal microbiota transplantation and other restoration approaches

Many organisms harbor redundant capacities for modulating metabolic health, raising hopes of therapeutic manipulation via the transfer of whole microbial communities through fecal microbiota transplantation (FMT). FMT has proven effective in combating recalcitrant infection by Clostridioides difficile¹²⁶ and has shown promise in inflammatory bowel disease¹²⁷. However, results of FMT for weight management and obesity in humans have been mixed, ranging from no clinical effect¹²⁸ to positive effects on secondary outcomes like reduced abdominal adiposity¹²⁹ and increased insulin response when coupled to diet intervention¹³⁰. Shai and colleagues examined whether FMT could be used to maintain weight loss after dietary intervention through transplant of an individual’s own microbiome at their point of maximal weight loss^131,132. Patients with obesity or dyslipidemia underwent a 6-month period of lifestyle-driven weight loss through exercise guidance and consuming either a healthy, Mediterranean, or green-Mediterranean diet (Mediterranean diet plus green tea and Wolffia globosa-based shake)¹³¹. For the subsequent 8 months, participants were given either capsules containing autologous feces collected at the end of the 6-month weight-loss phase or placebo. Only the green-Mediterranean diet group experienced significant changes in the gut microbiome during the weight-loss phase and, correspondingly, only in the green-Mediterranean group did autologous FMT attenuate weight regain, waist circumference gain, and insulin rebound versus placebo^131,132. Such data suggest that whole community replacement approaches are unlikely to be effective as a standalone solution, especially since autologous FMT evades some of the broader safety and ecosystem-matching challenges of heterologous FMT and therefore in many ways represents a best-case scenario.

Alternatively, microbiome restoration could be accomplished in more targeted ways that harness defined sets of microbes or microbial products robustly associated with health. Whereas the effects of traditional probiotics and prebiotics based on lactobacilli and bifidobacteria appear to have limited effects on weight or glucose homeostasis^,65–68,70, new candidates are under active development. For instance, administration of pasteurized A. muciniphila in animal models appears safe and can protect against diet-induced obesity, insulin resistance, and low-grade inflammation through positive effects on gut barrier integrity¹³³. Correspondingly, a recent exploratory study with 40 participants found that daily oral administration of 10¹⁰ pasteurized A. muciniphila over a 3-month period was well-tolerated, significantly reduced insulin resistance and insulinemia, and was associated with non-significant reductions in body fat and hip circumference versus baseline (P=0.09)¹³⁴. That pasteurized A. muciniphila shows increased efficacy versus live bacterium¹³³ facilitates safety and shelf-stability. In addition, recent discoveries that efficacy may be driven by outer membrane protein Amuc_1100¹³³ or secreted protein P9¹³⁵, with additional benefits for homeostatic immunity via phospholipid a15:0-i15:0 PE¹³⁶, provide targets for drug development and the prospect of predictable dosing kinetics, illuminating a path for translating basic discoveries into the clinic.

TOWARD MICROBIOME-AWARE WEIGHT MANAGEMENT

Key advances in our understanding of gut microbial influences on energy metabolism have been made in animal models, especially mice, that differ from humans in several aspects of anatomy, physiology, behavior, as well as microbiome structure and function¹³⁷. Therefore, realizing the therapeutic promise of the gut microbiome in ameliorating metabolic dysfunction will require substantial translational research. Intensified challenges in humans compared with animals models include high degrees of interindividual variation and longitudinal resilience in the human gut microbiome^13,138, low degree of ecological control driving day-to-day variation of the gut microbiome around these long-term signatures¹³⁹, and developmental plasticity exerting its influence over an extended human lifespan, resulting in greater temporal disconnect between host phenotypes and contributory microbial signatures. Developing protocols and synthetic microbial communities that can enable animal hosts to more faithfully replicate human-microbiome interactions in energy metabolism will be critical in narrowing the translational gap. It will also be useful to supplement murine studies with gnotobiotic studies in larger animals such as pigs, which more closely mimic humans in aspects of life history, physiology and microbiome. In vivo approaches, especially those involving bioreactors designed to mimic physiological conditions in the human gut¹⁴⁰, and ex vivo approaches, such as organoids derived from induced pluripotent stem cells or biopsied tissue¹⁴¹, are important complementary tools that have the benefit of greater control and reproducibility.

Difficulties in stably manipulating the human microbiome across individuals and environments suggest that one-size-fits-all and sledgehammer approaches like FMT are unlikely to be durably successful. Rather, greater long-term impacts will likely be achieved through personalized interventions targeting particular gut microbial functions and metabolites. In doing so, we will need to remain mindful of the pleiotropic effects of bacterial products. For instance, SCFA participate in colonic energy salvage while also minimizing hunger, increasing thermogenesis, and regulating embryonic tissue development, raising the possibility that SCFA-based interventions could have complex, even intergenerational³³, outcomes. Confronting these challenges will enable rational manipulation of the gut microbiome to be sufficiently long-lasting to improve health and be generalizable across individuals or personalized in an equitable manner.

We anticipate that within the next 10 years, many of the microbiome-modulating levers at our disposal will be revealed. Ongoing technical improvements in strain-level identification, culturing, genome editing, and metabolite identification will enable us to characterize with greater specificity the microbial players of interest and the ecological conditions that encourage their presence. For instance, recent studies have discovered substantial whole-genome divergence among A. muciniphila strains despite homogeneity in their 16S rDNA sequences¹⁴² that may contribute to explaining why strains exhibit differential impacts on inflammation¹⁴³. Continued delineation of the mechanisms through which the microbiome manipulates energy balance is expected to highlight promising targets for intervention in pathways regulating hunger and satiety, dietary energy harvest, energy allocation, and interactions between energy metabolism and inflammation. For instance, leveraging information about the thermogenic effect of secondary bile acids via TGR5-mediated activation of brown adipose tissue (Figs. 3–4), it was recently discovered that weight rebound after caloric restriction could be suppressed by supplementing mice with non-12α-hydroxylated bile acids or Parabacteroides distasonis, a microbe capable of producing these acids²². In addition, rapidly advancing knowledge of bacterial chemistry, including pervasive reciprocal interactions between the gut microbiome and therapeutic drugs^118,144, could suggest new microbiota-targeted approaches for enhancing the efficacy of existing drugs by potentiating or inhibiting bacterial biotransformations. Emergent knowledge of bacterial xenobiotic metabolism could eventually shape clinical decisions with regard to drug choice, dosage and/or coadministered agents, as has been proposed for non-metabolic drugs like digoxin, irinotecan and levodopa^145–147. By contrast, advances in our knowledge of small intestinal and mucosa-associated microbiota are proceeding more slowly, hampered by the greater invasiveness required in procuring such samples. Nevertheless, it is likely in the small intestine, where hosts and microbes compete directly for nutrients, and in the mucus layer, where host and microbial tissues come into closest proximity, that interactions are most consequential for energy metabolism and inflammation. Greater attention to these microbial communities is likely to reveal additional, high-impact targets for modulation.

Most research to date has focused on overweight and obesity rather than undernutrition. Yet existing evidence suggests that disruptions of the gut microbiome precede the onset of childhood malnutrition³¹, correlate with the severity of wasting²⁶ and stunting¹⁴⁸, and transmit weight loss phenotypes to gnotobiotic mice upon transplantation²⁵. Such data suggest a pivotal role for the gut microbiome in undernutrition and ripe opportunities for new research and the development of microbiota-focused therapies to ameliorate health for the one-in-three children worldwide who suffer from stunting (22.2%) and/or wasting (7.5%).

Although much remains to be discovered, promising microbiome-focused clinical trials targeting metabolic phenotypes associated with undernutrition or overnutrition are currently underway (Fig. 1). For instance, Gordon and colleagues developed several microbiota-directed complementary food (MDCF) recipes that were evaluated for their ability to mature the gut microbiome of undernourished Bangladeshi children to a healthy post-weaning profile^149,150. The lead MDCF prototype subsequently showed improved efficacy versus RUTF in Bangladeshi toddlers with moderate acute malnutrition, eliciting significant relative improvements in weight-for-length and weight-for-age z scores and changes in plasma proteins affecting bone growth and neurodevelopment¹⁵¹. Similarly, as discussed above, Shai and colleagues found that diet was an important support for the efficacy of autologous FMT in attenuating weight regain among patients with obesity or dyslipidemia who had undergone a 6-month period of lifestyle-driven weight loss, as FMT was only beneficial when combined with a specific green-Mediterranean diet¹³¹. As with MDCF, these data indicate that there is a potential for diet to act as an adjuvant for microbiome-targeted therapies. Future human trials should aim to standardize diet among participants by providing a fixed composition containing substrates to support microbial engraftment and the growth of beneficial microbes. Greater attention to dietary processing will also help to standardize delivery of nutrients to the densest microbial community in the colon²⁹. Notwithstanding, diet-independent routes of manipulation are also showing promise, as exemplified by Cani and colleagues reporting that probiotic administration of pasteurized A. muciniphila or delivery of specific Akkermansia-derived effector molecules, such as Amuc_1100, can reduce adiposity and insulin resistance¹³⁴. Collectively, such trials serve as important proofs-of-concept, showing that microbiome-aware therapies can compare favorably to and complement existing weight management schemes.

Outlook

Undernutrition and overnutrition weigh too heavily on public health not to leverage emerging transformational knowledge of gut microbial contributions to energy metabolism. Rational manipulation of the microbiome could increase the efficacy of existing therapies and generate novel treatments, giving physicians and patients new options for weight management. Several fundamental challenges will need to be overcome to realize this promise, including grappling with the ecological sensitivity of the gut microbiome, interindividual variability, developmental plasticity, and pleiotropic effects of the gut microbiome on human physiology. However, successes of recent clinical trials targeting both undernutrition and overnutrition highlight that microbiota-directed interventions for weight management are on the near-term horizon. Increased focus on the function and products of the gut microbiome rather than composition, enhanced efforts to establish the translational significance of discoveries made in animal models, and basic research to understand the physiological and ecological drivers of inflection points where the microbiome switches between phenotypic buffering and exacerbation are needed to effectively leverage the microbiome in the ongoing fight to improve global metabolic health.

Supplementary Material

Supplementary Table 1

Supplementary Table 1: Summary of human intervention studies evaluating effects of common diets and medical interventions applied for weight management on the gut microbiota using sequencing-based approaches

NIHMS2140595-supplement-Supplementary_Table_1.xlsx^{(89.5KB, xlsx)}

Supplementary Table 2

Supplementary Table 2: Studies included in meta-analysis

NIHMS2140595-supplement-Supplementary_Table_2.xlsx^{(73.1KB, xlsx)}

Supplementary Methods

Supplementary Methods: Description of methodology for meta-analysis

NIHMS2140595-supplement-Supplementary_Methods.pdf^{(35.4KB, pdf)}

ACKNOWLEDGEMENTS

This work was supported by grants from the National Science Foundation (BCS-1919892, BCS-2142073) and William F. Milton Fund to R.N.C. and from the National Institute of Allergy and Infectious Disease (R00AI47165) to J.E.B.

Footnotes

COMPETING INTERESTS

The authors have no competing interests to declare.

REFERENCES

1.Malnutrition. https://www.who.int/news-room/fact-sheets/detail/malnutrition.
2.Obesity and overweight. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.
3.Popkin BM, Corvalan C & Grummer-Strawn LM Dynamics of the double burden of malnutrition and the changing nutrition reality. Lancet 395, 65–74 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Anastasiou CA, Karfopoulou E & Yannakoulia M Weight regaining: From statistics and behaviors to physiology and metabolism. Metabolism 64, 1395–1407 (2015). [DOI] [PubMed] [Google Scholar]
5.Johannsen DL et al. Metabolic slowing with massive weight loss despite preservation of fat-free mass. J. Clin. Endocrinol. Metab 97, 2489–2496 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Fothergill E et al. Persistent metabolic adaptation 6 years after “The Biggest Loser” competition. Obesity 24, 1612–1619 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Guerrant RL, DeBoer MD, Moore SR, Scharf RJ & Lima AAM The impoverished gut--a triple burden of diarrhoea, stunting and chronic disease. Nat. Rev. Gastroenterol. Hepatol 10, 220–229 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Veenendaal MVE et al. Transgenerational effects of prenatal exposure to the 1944–45 Dutch famine. BJOG 120, 548–554 (2013). [DOI] [PubMed] [Google Scholar]
9.Bäckhed F et al. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl. Acad. Sci. U. S. A 101, 15718–15723 (2004). [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Turnbaugh PJ et al. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci. Transl. Med 1, 6ra14 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Cox LM & Blaser MJ Antibiotics in early life and obesity. Nat. Rev. Endocrinol 11, 182–190 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Cani PD et al. Microbial regulation of organismal energy homeostasis. Nat. Metab 1, 34–46 (2019). [DOI] [PubMed] [Google Scholar]
13.Gilbert JA et al. Current understanding of the human microbiome. Nat. Med 24, 392–400 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Al-Asmakh M & Zadjali F Use of germ-free animal models in microbiota-related research. J. Microbiol. Biotechnol 25, 1583–1588 (2015). [DOI] [PubMed] [Google Scholar]
15.Gheorghe CE et al. Investigating causality with fecal microbiota transplantation in rodents: applications, recommendations and pitfalls. Gut Microbes 13, 1941711 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Hayes CL et al. Commensal microbiota induces colonic barrier structure and functions that contribute to homeostasis. Sci. Rep 8, 14184 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Kawai Y & Morotomi M Intestinal enzyme activities in germfree, conventional, and gnotobiotic rats associated with indigenous microorganisms. Infect. Immun 19, 771–778 (1978). [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Slezak K et al. Association of germ-free mice with a simplified human intestinal microbiota results in a shortened intestine. Gut Microbes 5, 176–182 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Fouladi F et al. Sequence variant analysis reveals poor correlations in microbial taxonomic abundance between humans and mice after gnotobiotic transfer. ISME J. 14, 1809–1820 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Walter J, Armet AM, Finlay BB & Shanahan F Establishing or exaggerating causality for the gut microbiome: Lessons from human Microbiota-associated rodents. Cell 180, 221–232 (2020). [DOI] [PubMed] [Google Scholar]
21. Turnbaugh PJ et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006). This study established differences in the microbiomes between individuals who are lean and obese and that obese phenotypes were transmissible to gnotobiotic mice.
22.Li M et al. Gut microbiota-bile acid crosstalk contributes to the rebound weight gain after calorie restriction in mice. Nat. Commun 13, 2060 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Thaiss CA et al. Persistent microbiome alterations modulate the rate of post-dieting weight regain. Nature 540, 544–551 (2016). This study implicated microbiome contributions to weight regain after weight loss.
24.Koren O et al. Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell 150, 470–480 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Smith MI et al. Gut microbiomes of Malawian twin pairs discordant for kwashiorkor. Science 339, 548–554 (2013). This study found an immature gut microbiome configuration in a severe form of undernutrition.
26.Subramanian S et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Liou AP et al. Conserved shifts in the gut microbiota due to gastric bypass reduce host weight and adiposity. Sci. Transl. Med 5, 178ra41 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
28.von Schwartzenberg RJ et al. Caloric restriction disrupts the microbiota and colonization resistance. Nature 595, 272–277 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Carmody RN et al. Cooking shapes the structure and function of the gut microbiome. Nat. Microbiol 4, 2052–2063 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Cox LM et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell 158, 705–721 (2014). This study showed that gut microbiome perturbations in early life can have long-term consequences even when signatures recover.
31.McGuire MK & McGuire MA Microbiomes and Childhood Malnutrition: What Is the Evidence? Ann. Nutr. Metab 1–13 (2021). [DOI] [PubMed] [Google Scholar]
32.Vonaesch P et al. Stunted childhood growth is associated with decompartmentalization of the gastrointestinal tract and overgrowth of oropharyngeal taxa. Proc. Natl. Acad. Sci. U. S. A 115, E8489–E8498 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Kimura I et al. Maternal gut microbiota in pregnancy influences offspring metabolic phenotype in mice. Science 367, eaaw8429 (2020). This study demonstrated that exposure to SCFA in utero alters development in a manner that protects against the adult metabolic consequences of a high-fat diet.
34.Mishra A et al. Microbial exposure during early human development primes fetal immune cells. Cell 184, 3394–3409 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Schulfer AF et al. Intergenerational transfer of antibiotic-perturbed microbiota enhances colitis in susceptible mice. Nat Microbiol 3, 234–242 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Falony G et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016). [DOI] [PubMed] [Google Scholar]
37.Zhernakova A et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Cotillard A et al. A posteriori dietary patterns better explain variations of the gut microbiome than individual markers in the American Gut Project. Am. J. Clin. Nutr 115, 432–443 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Asnicar F et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat. Med 27, 321–332 (2021). This study correlated microbiome composition with dietary records and metabolic panels finding connections between specific microbes and health.
40. David LA et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014). This study demonstrated that microbiome composition responds to diet within days of administration.
41.Wu GD et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Oliver A et al. High-Fiber, Whole-Food Dietary Intervention Alters the Human Gut Microbiome but Not Fecal Short-Chain Fatty Acids. mSystems 6, (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Grembi JA et al. Gut microbiota plasticity is correlated with sustained weight loss on a low-carb or low-fat dietary intervention. Sci. Rep 10, 1405 (2020). This study demonstrated that weight loss success was correlated with the extent of microbiome response to diet.
44.Bisanz JE, Upadhyay V, Turnbaugh JA, Ly K & Turnbaugh PJ Meta-analysis reveals reproducible gut microbiome alterations in response to a high-fat diet. Cell Host Microbe 26, 265–272 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Dalby MJ, Ross AW, Walker AW & Morgan PJ Dietary Uncoupling of Gut Microbiota and Energy Harvesting from Obesity and Glucose Tolerance in Mice. Cell Rep. 21, 1521–1533 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Ang QY et al. Ketogenic Diets Alter the Gut Microbiome Resulting in Decreased Intestinal Th17 Cells. Cell 181, 1263–1275 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Ge L et al. Comparison of dietary macronutrient patterns of 14 popular named dietary programmes for weight and cardiovascular risk factor reduction in adults: systematic review and network meta-analysis of randomised trials. BMJ 369, m696 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
48. Koh A, De Vadder F, Kovatcheva-Datchary P & Bäckhed F From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites. Cell 165, 1332–1345 (2016). This review summarizes the origin and physiological functions of SCFAs.
49.Sonnenburg ED et al. Diet-induced extinctions in the gut microbiota compound over generations. Nature 529, 212–215 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Wastyk HC et al. Gut-microbiota-targeted diets modulate human immune status. Cell 184, 4137–4153 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Zhao L et al. Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Science 359, 1151–1156 (2018). [DOI] [PubMed] [Google Scholar]
52.Lancaster SM et al. Global, distinctive, and personal changes in molecular and microbial profiles by specific fibers in humans. Cell Host Microbe 30, 848–862 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Gibson GR et al. Expert consensus document: The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of prebiotics. Nat. Rev. Gastroenterol. Hepatol 14, 491–502 (2017). [DOI] [PubMed] [Google Scholar]
54.Baxter NT et al. Dynamics of Human Gut Microbiota and Short-Chain Fatty Acids in Response to Dietary Interventions with Three Fermentable Fibers. MBio 10, e02566–18 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Deehan EC et al. Precision Microbiome Modulation with Discrete Dietary Fiber Structures Directs Short-Chain Fatty Acid Production. Cell Host Microbe 27, 389–404 (2020). [DOI] [PubMed] [Google Scholar]
56.Chassaing B et al. Randomized Controlled-Feeding Study of Dietary Emulsifier Carboxymethylcellulose Reveals Detrimental Impacts on the Gut Microbiota and Metabolome. Gastroenterology 162, 743–756 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
57. Suez J et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature 514, 181–186 (2014). This study demonstrated that non-nutritive artificial sweeteners impacted microbiome composition with negative influences on metabolic health.
58. Chassaing B et al. Dietary emulsifiers impact the mouse gut microbiota promoting colitis and metabolic syndrome. Nature 519, 92–96 (2015). This study demonstrated that dietary emulsifiers compromise metabolic health via disruption of the gut microbiota and mucosal barrier.
59.Bian X et al. Saccharin induced liver inflammation in mice by altering the gut microbiota and its metabolic functions. Food Chem. Toxicol 107, 530–539 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Rodriguez-Palacios A et al. The artificial sweetener Splenda promotes gut Proteobacteria, dysbiosis, and myeloperoxidase reactivity in Crohn’s disease-like ileitis. Inflamm. Bowel Dis 24, 1005–1020 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Suez J et al. Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance. Cell 185, 3307–3328.e19 (2022). [DOI] [PubMed] [Google Scholar]
62.Serrano J et al. High-dose saccharin supplementation does not induce gut microbiota changes or glucose intolerance in healthy humans and mice. Microbiome 9, 11 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Thomson P, Santibañez R, Aguirre C, Galgani JE & Garrido D Short-term impact of sucralose consumption on the metabolic response and gut microbiome of healthy adults. Br. J. Nutr 122, 856–862 (2019). [DOI] [PubMed] [Google Scholar]
64.Cristofori F et al. Anti-Inflammatory and Immunomodulatory Effects of Probiotics in Gut Inflammation: A Door to the Body. Front. Immunol 12, 578386 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Tao Y-W, Gu Y-L, Mao X-Q, Zhang L & Pei Y-F Effects of probiotics on type II diabetes mellitus: a meta-analysis. J. Transl. Med 18, 30 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Kocsis T et al. Probiotics have beneficial metabolic effects in patients with type 2 diabetes mellitus: a meta-analysis of randomized clinical trials. Sci. Rep 10, 11787 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Álvarez-Arraño V & Martín-Peláez S Effects of Probiotics and Synbiotics on Weight Loss in Subjects with Overweight or Obesity: A Systematic Review. Nutrients 13, 3627 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Park S & Bae J-H Probiotics for weight loss: a systematic review and meta-analysis. Nutr. Res 35, 566–575 (2015). [DOI] [PubMed] [Google Scholar]
69.Eales J et al. Is consuming yoghurt associated with weight management outcomes? Results from a systematic review. Int. J. Obes 40, 731–746 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Lim S, Moon JH, Shin CM, Jeong D & Kim B Effect of Lactobacillus sakei, a probiotic derived from Kimchi, on body fat in Koreans with obesity: A randomized controlled study. Endocrinol. Metab. (Seoul) 35, 425–434 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Kapp JM & Sumner W Kombucha: a systematic review of the empirical evidence of human health benefit. Ann. Epidemiol 30, 66–70 (2019). [DOI] [PubMed] [Google Scholar]
72.Carmody RN et al. Diet dominates host genotype in shaping the murine gut microbiota. Cell Host Microbe 17, 72–84 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Christensen L et al. Prevotella abundance predicts weight loss success in healthy, overweight adults consuming a whole-grain diet ad libitum: a post hoc analysis of a 6-wk randomized controlled trial. J. Nutr 149, 2174–2181 (2019). [DOI] [PubMed] [Google Scholar]
74.Menni C et al. Gut microbiome diversity and high-fibre intake are related to lower long-term weight gain. Int. J. Obes 41, 1099–1105 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Jian C et al. Gut microbiota predicts body fat change following a low-energy diet: a PREVIEW intervention study. Genome Med. 14, 54 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Foster KR, Schluter J, Coyte KZ & Rakoff-Nahoum S The evolution of the host microbiome as an ecosystem on a leash. Nature 548, 43–51 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Carmody RN, Sarkar A & Reese AT Gut microbiota through an evolutionary lens. Science 372, 462–463 (2021). [DOI] [PubMed] [Google Scholar]
78. Zeevi D et al. Personalized Nutrition by Prediction of Glycemic Responses. Cell 163, 1079–1094 (2015). This study showed that models incorporating the gut microbiome improved prediction of postprandial glycemic response.
79.Johnson AJ et al. Daily Sampling Reveals Personalized Diet-Microbiome Associations in Humans. Cell Host Microbe 25, 789–802 (2019). [DOI] [PubMed] [Google Scholar]
80.Lai Z-L et al. Fecal microbiota transplantation confers beneficial metabolic effects of diet and exercise on diet-induced obese mice. Sci. Rep 8, 15625 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
81.Cronin O et al. A prospective metagenomic and metabolomic analysis of the impact of exercise and/or whey protein supplementation on the gut microbiome of sedentary adults. mSystems 3, e00044–18 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
82.Kang SS et al. Diet and exercise orthogonally alter the gut microbiome and reveal independent associations with anxiety and cognition. Mol. Neurodegener 9, 36 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
83.Mohr AE et al. The athletic gut microbiota. J. Int. Soc. Sports Nutr 17, 24 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
84.Allen JM et al. Voluntary and forced exercise differentially alters the gut microbiome in C57BL/6J mice. J. Appl. Physiol 118, 1059–1066 (2015). [DOI] [PubMed] [Google Scholar]
85.Matsumoto M et al. Voluntary running exercise alters microbiota composition and increases n-butyrate concentration in the rat cecum. Biosci. Biotechnol. Biochem 72, 572–576 (2008). [DOI] [PubMed] [Google Scholar]
86.Allen JM et al. Exercise alters gut microbiota composition and function in lean and obese humans. Med. Sci. Sports Exerc 50, 747–757 (2018). [DOI] [PubMed] [Google Scholar]
87. Scheiman J et al. Meta-omics analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism. Nat. Med 25, 1104–1109 (2019). This study identified lactate-consuming, propionate-producing microbes as positively influencing exercise performance.
88.Zhao X et al. Response of gut microbiota to metabolite changes induced by endurance exercise. Front. Microbiol 9, 765 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
89.Clarke SF et al. Exercise and associated dietary extremes impact on gut microbial diversity. Gut 63, 1913–1920 (2014). [DOI] [PubMed] [Google Scholar]
90.Barton W et al. The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level. Gut 67, 625–633 (2018). [DOI] [PubMed] [Google Scholar]
91.Petersen LM et al. Community characteristics of the gut microbiomes of competitive cyclists. Microbiome 5, 98 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
92.Durk RP et al. Gut Microbiota Composition Is Related to Cardiorespiratory Fitness in Healthy Young Adults. Int. J. Sport Nutr Exerc. Metab 29, 249–253 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
93.Estaki M et al. Cardiorespiratory fitness as a predictor of intestinal microbial diversity and distinct metagenomic functions. Microbiome 4, 42 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
94.Morita H et al. Bacteroides uniformis and its preferred substrate, α-cyclodextrin, enhance endurance exercise performance in mice and human males. Sci. Adv 9, eadd2120 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
95.Liu Y et al. Gut Microbiome Fermentation Determines the Efficacy of Exercise for Diabetes Prevention. Cell Metab. 31, 77–91.e5 (2020). [DOI] [PubMed] [Google Scholar]
96.Bressa C et al. Differences in gut microbiota profile between women with active lifestyle and sedentary women. PLoS One 12, e0171352 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
97.Munukka E et al. Six-Week Endurance Exercise Alters Gut Metagenome That Is not Reflected in Systemic Metabolism in Overweight Women. Front. Microbiol 9, 2323 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
98.Carmody RN & Baggish AL Working out the bugs: microbial modulation of athletic performance. Nat. Metab 1, 658–659 (2019). [DOI] [PubMed] [Google Scholar]
99.Hoffman-Goetz L, Pervaiz N, Packer N & Guan J Freewheel training decreases pro- and increases anti-inflammatory cytokine expression in mouse intestinal lymphocytes. Brain Behav. Immun 24, 1105–1115 (2010). [DOI] [PubMed] [Google Scholar]
100.Ismail AS et al. γδ intraepithelial lymphocytes are essential mediators of host–microbial homeostasis at the intestinal mucosal surface. Proc. Natl. Acad. Sci. U. S. A 108, 8743–8748 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
101.Jeukendrup AE et al. Relationship between gastrointestinal complaints and endotoxaemia, cytokine release and the acute-phase reaction during and after a long-distance triathlon in highly trained men. Clin. Sci 98, 47–55 (2000). [PubMed] [Google Scholar]
102.Lira FS et al. Endotoxin levels correlate positively with a sedentary lifestyle and negatively with highly trained subjects. Lipids Health Dis. 9, 82 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
103.Meissner M et al. Voluntary wheel running increases bile acid as well as cholesterol excretion and decreases atherosclerosis in hypercholesterolemic mice. Atherosclerosis 218, 323–329 (2011). [DOI] [PubMed] [Google Scholar]
104.Yardeni T et al. Host mitochondria influence gut microbiome diversity: A role for ROS. Sci. Signal 12, (2019). [DOI] [PubMed] [Google Scholar]
105.Song BK, Cho KO, Jo Y, Oh JW & Kim YS Colon transit time according to physical activity level in adults. J. Neurogastroenterol. Motil 18, 64–69 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
106.Asnicar F et al. Blue poo: impact of gut transit time on the gut microbiome using a novel marker. Gut 70, 1665–1674 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
107.van Wijck K et al. Exercise-induced splanchnic hypoperfusion results in gut dysfunction in healthy men. PLoS One 6, e22366 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
108.Murphy RM, Watt MJ & Febbraio MA Metabolic communication during exercise. Nat. Metab 2, 805–816 (2020). [DOI] [PubMed] [Google Scholar]
109.O’Brien PE et al. Long-Term Outcomes After Bariatric Surgery: a Systematic Review and Meta-analysis of Weight Loss at 10 or More Years for All Bariatric Procedures and a Single-Centre Review of 20-Year Outcomes After Adjustable Gastric Banding. Obes. Surg 29, 3–14 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
110.Kong L-C et al. Gut microbiota after gastric bypass in human obesity: increased richness and associations of bacterial genera with adipose tissue genes. Am. J. Clin. Nutr 98, 16–24 (2013). [DOI] [PubMed] [Google Scholar]
111.Paganelli FL et al. Roux-Y Gastric Bypass and Sleeve Gastrectomy directly change gut microbiota composition independent of surgery type. Sci. Rep 9, 10979 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
112.Li JV et al. Roux-en-Y gastric bypass-induced bacterial perturbation contributes to altered host766 bacterial co-metabolic phenotype. Microbiome 9, 139 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
113.Tremaroli V et al. Roux-en-Y Gastric Bypass and Vertical Banded Gastroplasty Induce Long-Term Changes on the Human Gut Microbiome Contributing to Fat Mass Regulation. Cell Metab. 22, 228–238 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
114.Shen N et al. Longitudinal changes of microbiome composition and microbial metabolomics after surgical weight loss in individuals with obesity. Surg. Obes. Relat. Dis 15, 1367–1373 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
115.Shantavasinkul PC, Omotosho P, Corsino L, Portenier D & Torquati A Predictors of weight regain in patients who underwent Roux-en-Y gastric bypass surgery. Surg. Obes. Relat. Dis 12, 1640–1645 (2016). [DOI] [PubMed] [Google Scholar]
116.Gutiérrez-Repiso C et al. Gut microbiota specific signatures are related to the successful rate of bariatric surgery. Am. J. Transl. Res 11, 942–952 (2019). [PMC free article] [PubMed] [Google Scholar]
117.Fouladi F et al. The Role of the Gut Microbiota in Sustained Weight Loss Following Roux-en-Y Gastric Bypass Surgery. Obes. Surg 29, 1259–1267 (2019). [DOI] [PubMed] [Google Scholar]
118.Spanogiannopoulos P, Bess EN, Carmody RN & Turnbaugh PJ The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism. Nat. Rev. Microbiol 14, 273–287 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
119.Maier L et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555, 623–628 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
120.Wu H et al. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat. Med 23, 850–858 (2017). [DOI] [PubMed] [Google Scholar]
121. Forslund K et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015). This study established the interactions of metformin and gut microbes impacting efficacy for diabetes treatment.
122.Zhang X et al. Effects of Acarbose on the Gut Microbiota of Prediabetic Patients: A Randomized, Double-blind, Controlled Crossover Trial. Diabetes Ther. 8, 293–307 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
123.Balaich J et al. The human microbiome encodes resistance to the antidiabetic drug acarbose. Nature 600, 110–115 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
124.Vieira-Silva S et al. Statin therapy is associated with lower prevalence of gut microbiota dysbiosis. Nature 581, 310–315 (2020). [DOI] [PubMed] [Google Scholar]
125.Wilmanski T et al. Heterogeneity in statin responses explained by variation in the human gut microbiome. Med 3, 388–405 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
126.Baunwall SMD et al. Faecal microbiota transplantation for recurrent Clostridioides difficile infection: An updated systematic review and meta-analysis. EClinicalMedicine 29–30, 100642 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
127.Imdad A et al. Fecal transplantation for treatment of inflammatory bowel disease. Cochrane Database Syst. Rev 11, CD012774 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
128.Yu EW et al. Fecal microbiota transplantation for the improvement of metabolism in obesity: The FMT-TRIM double-blind placebo-controlled pilot trial. PLoS Med. 17, e1003051 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
129.Leong KSW et al. Effects of Fecal Microbiome Transfer in Adolescents With Obesity: The Gut Bugs Randomized Controlled Trial. JAMA Netw Open 3, e2030415 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
130.Mocanu V et al. Fecal microbial transplantation and fiber supplementation in patients with severe obesity and metabolic syndrome: a randomized double-blind, placebo-controlled phase 2 trial. Nat. Med 27, 1272–1279 (2021). [DOI] [PubMed] [Google Scholar]
131.Rinott E et al. Effects of Diet-Modulated Autologous Fecal Microbiota Transplantation on Weight Regain. Gastroenterology 160, 158–173 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
132. Rinott E et al. Autologous fecal microbiota transplantation can retain the metabolic achievements of dietary interventions. Eur. J. Intern. Med 92, 17–23 (2021). This study demonstrated potential for self-fecal transplant combined with diet for maintenance of weight loss.
133.Plovier H et al. A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice. Nat. Med 23, 107–113 (2017). [DOI] [PubMed] [Google Scholar]
134.Depommier C et al. Supplementation with Akkermansia muciniphila in overweight and obese human volunteers: a proof-of-concept exploratory study. Nat. Med 25, 1096–1103 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
135. Yoon HS et al. Akkermansia muciniphila secretes a glucagon-like peptide-1-inducing protein that improves glucose homeostasis and ameliorates metabolic disease in mice. Nat. Microbiol 6, 563–573 (2021). This human intervention study showed daily A. muciniphila as having beneficial effects on metabolic health.
136.Bae M et al. Akkermansia muciniphila phospholipid induces homeostatic immune responses. Nature 1–6 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
137.Beresford-Jones BS et al. The Mouse Gastrointestinal Bacteria Catalogue enables translation between the mouse and human gut microbiotas via functional mapping. Cell Host Microbe 30, 124–138 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
138.Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK & Knight R Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
139.David LA et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 15, R89 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
140.Arnold JW, Roach J & Azcarate-Peril MA Emerging technologies for gut microbiome research. Trends Microbiol. 24, 887–901 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
141.Yu H et al. The contributions of human mini-intestines to the study of intestinal physiology and pathophysiology. Annu. Rev. Physiol 79, 291–312 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
142.Karcher N et al. Genomic diversity and ecology of human-associated Akkermansia species in the gut microbiome revealed by extensive metagenomic assembly. Genome Biol. 22, 209 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
143.Liu Q et al. Akkermansia muciniphila Exerts Strain-Specific Effects on DSS-Induced Ulcerative Colitis in Mice. Front. Cell. Infect. Microbiol 11, 698914 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
144.Zimmermann M, Zimmermann-Kogadeeva M, Wegmann R & Goodman AL Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature 570, 462–467 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
145.Haiser HJ et al. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 341, 295–298 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
146.Wallace BD et al. Alleviating cancer drug toxicity by inhibiting a bacterial enzyme. Science 330, 831–835 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
147.Maini Rekdal V, Bess EN, Bisanz JE, Turnbaugh PJ & Balskus EP Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism. Science 364, (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
148.Gough EK et al. Linear growth faltering in infants is associated with Acidaminococcus sp. and community-level changes in the gut microbiota. Microbiome 3, 24 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
149.Raman AS et al. A sparse covarying unit that describes healthy and impaired human gut microbiota development. Science 365, (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
150.Gehrig JL et al. Effects of microbiota-directed foods in gnotobiotic animals and undernourished children. Science 365, eaau4732 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
151.Chen RY et al. A Microbiota-Directed Food Intervention for Undernourished Children. N. Engl. J. Med 384, 1517–1528 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
152. Semova I et al. Microbiota regulate intestinal absorption and metabolism of fatty acids in the zebrafish. Cell Host Microbe 12, 277–288 (2012). This trial showed that a microbiome-targeted dietary intervention could improve health in undernourished children.
153.Martinez-Guryn K et al. Small intestine Microbiota regulate host digestive and absorptive adaptive responses to dietary lipids. Cell Host Microbe 23, 458–469 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
154.Chadaideh KS & Carmody RN Host-microbial interactions in the metabolism of different dietary fats. Cell Metab. 33, 857–872 (2021). [DOI] [PubMed] [Google Scholar]
155.Carmody RN & Wrangham RW The energetic significance of cooking. J. Hum. Evol 57, 379–391 (2009). [DOI] [PubMed] [Google Scholar]
156.Carmody RN, Weintraub GS & Wrangham RW Energetic consequences of thermal and nonthermal food processing. Proc. Natl. Acad. Sci. U. S. A 108, 19199–19203 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
157.Cummings JH & Macfarlane GT Role of intestinal bacteria in nutrient metabolism. Clin. Nutr 16, 3–11 (1997). [DOI] [PubMed] [Google Scholar]
158.McNeil NI The contribution of the large intestine to energy supplies in man. Am. J. Clin. Nutr 39, 338–342 (1984). [DOI] [PubMed] [Google Scholar]
159.den Besten G et al. Short-chain fatty acids protect against high-fat diet-induced obesity via a PPARγ-dependent switch from lipogenesis to fat oxidation. Diabetes 64, 2398–2408 (2015). [DOI] [PubMed] [Google Scholar]
160.Lu Y et al. Short chain fatty acids prevent high-fat-diet-induced obesity in mice by regulating G protein-coupled receptors and gut microbiota. Sci. Rep 6, 37589 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
161.van der Hee B & Wells JM Microbial regulation of host physiology by short-chain fatty acids. Trends Microbiol. 29, 700–712 (2021). [DOI] [PubMed] [Google Scholar]
162.Hong Y-H et al. Acetate and propionate short chain fatty acids stimulate adipogenesis via GPCR43. Endocrinology 146, 5092–5099 (2005). [DOI] [PubMed] [Google Scholar]
163.Jocken JWE et al. Short-Chain fatty acids differentially affect intracellular lipolysis in a human white adipocyte model. Front. Endocrinol 8, 372 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
164.Jia Y et al. Butyrate stimulates adipose lipolysis and mitochondrial oxidative phosphorylation through histone hyperacetylation-associated β3-adrenergic receptor activation in high-fat diet-induced obese mice. Exp. Physiol 102, 273–281 (2017). [DOI] [PubMed] [Google Scholar]
165.Gao Z et al. Butyrate improves insulin sensitivity and increases energy expenditure in mice. Diabetes 58, 1509–1517 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
166.Cummings JH & Macfarlane GT The control and consequences of bacterial fermentation in the human colon. J. Appl. Bacteriol 70, 443–459 (1991). [DOI] [PubMed] [Google Scholar]
167.De Vadder F et al. Microbiota-generated metabolites promote metabolic benefits via gut-brain neural circuits. Cell 156, 84–96 (2014). [DOI] [PubMed] [Google Scholar]
168.Kondo T, Kishi M, Fushimi T & Kaga T Acetic acid upregulates the expression of genes for fatty acid oxidation enzymes in liver to suppress body fat accumulation. J. Agric. Food Chem 57, 5982–5986 (2009). [DOI] [PubMed] [Google Scholar]
169.Sahuri-Arisoylu M et al. Reprogramming of hepatic fat accumulation and “browning” of adipose tissue by the short-chain fatty acid acetate. Int. J. Obes 40, 955–963 (2016). [DOI] [PubMed] [Google Scholar]
170.Sinal CJ et al. Targeted disruption of the nuclear receptor FXR/BAR impairs bile acid and lipid homeostasis. Cell 102, 731–744 (2000). [DOI] [PubMed] [Google Scholar]
171.Ridlon JM, Kang DJ, Hylemon PB & Bajaj JS Bile acids and the gut microbiome. Curr. Opin. Gastroenterol 30, 332–338 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
172.Pathak P et al. Intestine farnesoid X receptor agonist and the gut microbiota activate G-protein bile acid receptor-1 signaling to improve metabolism. Hepatology 68, 1574–1588 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
173.Chambers ES et al. Effects of targeted delivery of propionate to the human colon on appetite regulation, body weight maintenance and adiposity in overweight adults. Gut 64, 1744–1754 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
174.Goswami C, Iwasaki Y & Yada T Short-chain fatty acids suppress food intake by activating vagal afferent neurons. J. Nutr. Biochem 57, 130–135 (2018). [DOI] [PubMed] [Google Scholar]
175.Tolhurst G et al. Short-chain fatty acids stimulate glucagon-like peptide-1 secretion via the G-protein–coupled receptor FFAR2. Diabetes 61, 364–371 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
176.Psichas A et al. The short chain fatty acid propionate stimulates GLP-1 and PYY secretion via free fatty acid receptor 2 in rodents. Int. J. Obes 39, 424–429 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
177.Frost G et al. The short-chain fatty acid acetate reduces appetite via a central homeostatic mechanism. Nat. Commun 5, 3611 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
178.Hsiao WWL, Metz C, Singh DP & Roth J The microbes of the intestine: an introduction to their metabolic and signaling capabilities. Endocrinol. Metab. Clin. North Am 37, 857–871 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
179.Strandwitz P Neurotransmitter modulation by the gut microbiota. Brain Res. 1693, 128–133 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
180.Yano JM et al. Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 161, 264–276 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
181.Heijtz RD et al. Normal gut microbiota modulates brain development and behavior. Proceedings of the National Academy of Sciences 108, 3047–3052 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
182.Swartz TD, Duca FA, de Wouters T, Sakar Y & Covasa M Up-regulation of intestinal type 1 taste receptor 3 and sodium glucose luminal transporter-1 expression and increased sucrose intake in mice lacking gut microbiota. Br. J. Nutr 107, 621–630 (2012). [DOI] [PubMed] [Google Scholar]
183.Duca FA, Swartz TD, Sakar Y & Covasa M Increased oral detection, but decreased intestinal signaling for fats in mice lacking gut microbiota. PLoS One 7, e39748 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
184.de Wouters d’Oplinter A et al. Gut microbes participate in food preference alterations during obesity. Gut Microbes 13, 1959242 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
185.Trevelline BK & Kohl KD The gut microbiome influences host diet selection behavior. Proc. Natl. Acad. Sci. U. S. A 119, e2117537119 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
186.Alcock J, Maley CC & Aktipis CA Is eating behavior manipulated by the gastrointestinal microbiota? Evolutionary pressures and potential mechanisms. Bioessays 36, 940–949 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
187.Leitão-Gonçalves R et al. Commensal bacteria and essential amino acids control food choice behavior and reproduction. PLoS Biol. 15, e2000862 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
188.Hotamisligil GS Inflammation and metabolic disorders. Nature 444, 860–867 (2006). [DOI] [PubMed] [Google Scholar]
189.Renz H & Skevaki C Early life microbial exposures and allergy risks: opportunities for prevention. Nat. Rev. Immunol 21, 177–191 (2021). [DOI] [PubMed] [Google Scholar]
190.Pronovost GN & Hsiao EY Perinatal Interactions between the Microbiome, Immunity, and Neurodevelopment. Immunity 50, 18–36 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
191.Cani PD et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 56, 1761–1772 (2007). [DOI] [PubMed] [Google Scholar]
192.Vijay-Kumar M et al. Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Science 328, 228–231 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
193.Camilleri M Leaky gut: mechanisms, measurement and clinical implications in humans. Gut 68, 1516–1526 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
194.Rohr MW, Narasimhulu CA, Rudeski-Rohr TA & Parthasarathy S Negative effects of a high-fat diet on intestinal permeability: a review. Adv. Nutr 11, 77–91 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1

NIHMS2140595-supplement-Supplementary_Table_1.xlsx^{(89.5KB, xlsx)}

Supplementary Table 2

Supplementary Table 2: Studies included in meta-analysis

NIHMS2140595-supplement-Supplementary_Table_2.xlsx^{(73.1KB, xlsx)}

Supplementary Methods

Supplementary Methods: Description of methodology for meta-analysis

NIHMS2140595-supplement-Supplementary_Methods.pdf^{(35.4KB, pdf)}

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Roles of the gut microbiome in weight management

Rachel N Carmody

Jordan E Bisanz

Abstract

INTRODUCTION

Figure 1. Reciprocal influences between the gut microbiome and key lifestyle and clinical approaches for weight management.

GUT MICROBIOME AND ENERGY METABOLISM

Box 1: Influences of the gut microbiome on energy intake.

Figure 2. Gut microbiome enhances dietary energy harvest.

Figure 3. Gut microbiome modulates energy utilization.

Box 2: Interactions of the gut microbiome with low-grade inflammation.

Figure 4. Mechanisms of gut microbial influence on host energy status.

DIETS AND WEIGHT MANAGEMENT

Dietary properties shaping gut microbial contributions to weight management

Specific diets and gut microbial contributions to weight management

Figure 5. Meta-analysis of the effects of weight management interventions on gut microbiome composition.

Diet outcomes

PHYSICAL ACTIVITY AND WEIGHT MANAGEMENT

CLINICAL PATHS AND WEIGHT MANAGEMENT

Surgical approaches

Drugs

Fecal microbiota transplantation and other restoration approaches

TOWARD MICROBIOME-AWARE WEIGHT MANAGEMENT

Outlook

Supplementary Material

ACKNOWLEDGEMENTS

Footnotes

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Cite

Add to Collections

PERMALINK

Roles of the gut microbiome in weight management

Rachel N Carmody

Jordan E Bisanz

Abstract

INTRODUCTION

Figure 1. Reciprocal influences between the gut microbiome and key lifestyle and clinical approaches for weight management.

GUT MICROBIOME AND ENERGY METABOLISM

Box 1: Influences of the gut microbiome on energy intake.

Figure 2. Gut microbiome enhances dietary energy harvest.

Figure 3. Gut microbiome modulates energy utilization.

Box 2: Interactions of the gut microbiome with low-grade inflammation.

Figure 4. Mechanisms of gut microbial influence on host energy status.

DIETS AND WEIGHT MANAGEMENT

Dietary properties shaping gut microbial contributions to weight management

Specific diets and gut microbial contributions to weight management

Figure 5. Meta-analysis of the effects of weight management interventions on gut microbiome composition.

Diet outcomes

PHYSICAL ACTIVITY AND WEIGHT MANAGEMENT

CLINICAL PATHS AND WEIGHT MANAGEMENT

Surgical approaches

Drugs

Fecal microbiota transplantation and other restoration approaches

TOWARD MICROBIOME-AWARE WEIGHT MANAGEMENT

Outlook

Supplementary Material

ACKNOWLEDGEMENTS

Footnotes

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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