Abstract
Background: It has been suggested that the dysfunction of the gut microbiome can have deleterious effects on the regulation of body weight and adiposity by affecting energy metabolism. In this context, gut bacterial profiling studies have contributed to characterize specific bacteria associated with obesity. This review covers the information driven by gut bacterial profiling analyses and emphasizes the potential application of this knowledge in precision nutrition strategies for obesity understanding and weight loss management. Summary: Gut bacterial profiling studies have identified bacterial families that are more abundant in obese than in nonobese individuals (i.e., Prevotellaeae, Ruminococcaceae, and Veillonellaceae) as well as other families that have been repeatedly found more abundant in nonobese people (i.e., Christensenellaceae and Coriobacteriaceae), suggesting that an increase in their relative amount could be an interesting target in weight-loss treatments. Also, some gut-derived metabolites have been related to the regulation of body weight, including short-chain fatty acids, trimethylamine-N-oxide, and branched-chain and aromatic amino acids. Moreover, gut microbiota profiles may play a role in determining weight loss responses to specific nutritional treatments for the precise management of obesity. Thus, incorporating gut microbiota features may improve the performance of integrative models to predict weight loss outcomes. Key Messages: The application of gut bacterial profiling information is of great value for precision nutrition in metabolic diseases since it contributes to the understanding of the role of the gut microbiota in obesity onset and progression, facilitates the identification of potential microorganism targets, and allows the personalization of tailored weight loss diets as well as the prediction of adiposity outcomes based on the gut bacterial profiling of each individual. Integrating microbiota information with other omics knowledge (genetics, epigenetics, transcriptomics, proteomics, and metabolomics) may provide a more comprehensive understanding of the molecular and physiological events underlying obesity and adiposity outcomes for precision nutrition.
Introduction
In the last years, research is unveiling that a disequilibrium in gut microbiota composition (which is generally called as dysbiosis) could be involved in a higher risk to develop metabolic diseases in humans including obesity [1]. Although the term dysbiosis has not been well defined, it can be identified by a reduction in bacterial diversity and richness or by an increase in the proportion of potential pathobionts [2].
The mechanisms are still unclear, but a dysfunction in gut microbiota is probably linked with an altered gut metabolome [3]. Also, an increase in gut permeability has been suggested, which can be mediated by an impairment in the function of the tight junction proteins and an excessive flux across the pore, which is known as leaky gut [4]. It can contribute to an increased uptake of potential pro-inflammatory molecules like lipopolysaccharides (LPS), trimethylamine oxide (TMAO), and specific membrane proteins of the bacteria, which are able to induce pro-inflammatory mechanisms not only in the gut but also in the liver and other organs like the brain or the adipose tissue [5].
In this context, it has been suggested that the dysfunction of the gut microbiome can have deleterious effects on the regulation of body weight and adiposity, by acting at several levels: (1) dysregulation of the mechanisms that control appetite and satiety, both in the gut (i.e., orexigenic peptides), in adipose tissue (i.e., leptin secretion) or the hypothalamus [6]; (2) alterations of the mechanisms that regulate mood and behavior, including the HPA axis and the levels of serotonin, dopamine, and GABA [7]; (3) facilitation of the development of insulin resistance by generating a low-grade inflammatory milieu [8]; (4) increase of the extraction of calories from the diet by enhancing the capacity to ferment fibers and produce an excessive amount of short-chain fatty acids (SCFA) like valerate, propionate, butyrate, and acetate [9]; (5) increase of the absorption of specific nutrients by modifying the expression and function of different solute carriers (SLCs) in the intestinal cells [10].
Furthermore, a number of studies have reported differences in the composition of the gut microbiome when comparing the response to a weight-loss intervention in obese and nonobese individuals [11]. This information can be valuable in the application of metagenomics for precision nutrition [12]. For example, it is being used in the design of microbiota tests that could help detect individuals with more microbiota-related risk to develop obesity and its comorbidities, but it could also be used in the personalization of weight loss and weight maintenance strategies: (1) whether specific bacteria of the gut microbiota are related to the response to a particular weight-loss diet or (2) whether modifications of the gut microbiota composition can help normalize some of the physiological mechanism implicated in the regulation of appetite and body weight.
Thus, identifying obesity microbial biomarkers associated with body weight control in overweight and obesity may provide fertile targets for the precise management of obesity (Fig. 1). This review covers the information driven by gut bacterial profiling analyses and emphasizes the potential application of this knowledge in precision nutrition strategies for obesity understanding and weight loss management.
Search and application of gut bacterial profiling information in precision nutrition for obesity and weight loss management.
Search and application of gut bacterial profiling information in precision nutrition for obesity and weight loss management.
Results
Microbiota Information in Obesity Development
Genera, Families, Species, and Enterotypes Associated with Obesity
In the last years, gut bacterial profiling studies have evidenced the role of the gut microbiota in the onset and progression of obesity. In humans, it is difficult to infer causality from these studies, but in rodents it has been demonstrated that the transplantation of fecal microbiota from obese to nonobese mice is able to induce weight and adiposity gain in the receptors [13].
As the gut microbiota of rodents and humans in rather different, we are going to focus on human studies. It is important to highlight that the gut microbiome is a complex ecosystem that presents a big interindividual variability. This complexity is a limitation when trying to find common factors affecting big populations.
In European populations, there are some bacteria that have been repeatedly linked with higher body weight and adiposity. One of them is the enterotype Prevotella, which is characterized by a high proportion of species from the genus Prevotella [14]. Studies from our group have arisen some families that are more abundant in obese than in nonobese individuals in Spanish population, including Prevotellaeae, Ruminococcaceae, and Veillonellaceae [15]. On the contrary, some specific bacterial families, as Christensenellaceae and Coriobacteriaceae, have been repeatedly found more abundant in nonobese people, suggesting that an increase in their relative amount could be an interesting objective in weight-loss treatments [16]. However, fecal samples may be not necessarily representative of a given patient, particularly if they are transversely taken once. Thus, longitudinal prospective studies and sequencing technologies to characterize microbial communities in different tissues could be more appropriate to analyze the implications of the gut microbes that would have a larger impact on human adiposity. Moreover, simple associations between gut microbiota composition (high/low abundances) and obesity should be taken with caution since they may be disarranged secondarily to some other intermediate factors and be symptomatic, rather than causal. Therefore, mechanistic studies may be required to infer causality, but in humans it is difficult due to the involvement of intrinsic and extrinsic issues modulating gut microbiota.
The abundance of specific species has been also associated with better metabolic traits, as is the case of: (1) Akkermansia muciniphila, a bacterium from the phylum Verrucomicrobiota, which modulates obesity by regulating energy metabolism and improving insulin sensitivity and glucose utilization. In addition, this species ameliorates low-grade inflammation by different mechanisms [17]; (2) Faecalibacterium prausnitzii, one of the most abundant anaerobic bacteria in the human gut microbiota, is an active contributor to intestinal health and maintenance due to its immunomodulatory properties [18]; (3) species of the genus Ruminococcus (i.e., R. callidus and unclassified species from the Ruminococcaceae family) are involved in shaping core microbiota and are associated with reduced cardiometabolic risk in obesity conditions [19].
Lifestyle (Diet, Exercise, Sleep, Emotions), Microbiota, and Obesity
The gut microbiota interacts with and depends on many factors that are not easy to be quantified in humans, such as diet composition, physical activity (PA), antibiotic treatments, drugs, stress and anxiety, sleep duration and quality, hormones (i.e., estrogens in women), pollutants, and many others. Most of these factors are well known as contributors to metabolic dysfunctions, and it is difficult to establish whether the changes in gut microbiota are a consequence of them or they are independent issues. The impact of each of these factors on gut microbiota composition in the context of obesity has been recently reviewed [20].
This fact induces to think that only by modifying the gut microbiota profile it is difficult to importantly affect body weight and adiposity. On the contrary, it seems more plausible that the strategies to control body weight must consider most of them and try to act in a multimodal way. In reality, the human being must be viewed as a holobiont and all the factors that affect the equilibrium of this complex ecological unit are affecting the response of the whole.
Gut-Derived Metabolites
In the last years, a big effort is being made to identify and study the metabolites that are produced by the gut microorganisms. Some of them have been related to the regulation of body weight by different reasons, such as: (1) SCFAs: acetate, propionate, and butyrate derive from the microbial fermentation of nondigestible dietary fibers and constitute an energy source for both microbiota and the host. Additionally, SCFAs may directly or indirectly affect the brain and adipose tissue thus modulating lipid metabolism, adipokine secretion, insulin sensitivity, and inflammation [21]. (2) Trimethylamine-N-oxide (TMAO): TMAO is a metabolite generated from the oxidation of trimethylamine in the gut microbiota, which has been suggested to play a role in obesity regulation and adipose tissue beiging, which refers to the process of converting the white adipose tissue to “brown-like” adipocytes known as beige cells [22]. (3) Branched-chain and aromatic amino acids: It has been reported that the concentrations of branched and aromatic amino acid-derived microbial metabolites are altered in obesity and metabolic disorders including insulin resistance, hyperglycemia, fatty liver, hyperlipidemia, and elevated circulating inflammatory factors [23]. (4) Bile acids derivatives and other metabolites derived from the degradation of carnitine, polyphenols, and purines are among the gut microbiota-derived metabolites which show alterations in obesity [23].
Microbiota as Weight Loss Predictor after Dietary Intervention
Gut microbiota profiles may play a role in determining weight loss responses to specific nutritional treatments for the management of obesity. Thus, incorporating gut microbiota features may improve the performance of integrative models to predict weight loss outcomes [24]. In this context, a multi-omics model identified 64% of nonresponders to grain-based diets by integrating gut microbiome signatures including butyrate-producing species [25]. Also, the baseline gut microbiota composition was found to outperform other conventional factors as predictors of individual weight loss trajectories, being related to the magnitude of changes in abundances of bacterial species during energy-restricted dieting [26].
Therefore, the gut microbiota configuration can help prescribe tailor-based precision nutrition strategies to reach better adiposity reductions and improve the metabolic status of people with obesity at the individual level [27]. Particular examples of studies analyzing microbiota signatures associated with weight loss after following different lifestyle interventions (including diet and PA) are summarized in Table 1. Overall, it is shown that abundances or decreases of specific microbial species at baseline can be useful biomarker tools to estimate the success or resistance to nutritional programs (mainly hypocaloric diets varying in macronutrient/micronutrient content with and without fiber supplementation and additional exercise training) in terms of body weight reductions, adiposity losses, and weight regain outcomes (Table 1). Many of these microorganisms regulate key metabolic pathways related to energy homeostasis, nutrient utilization, and cell integrity.
Summary of specific microbiota signatures associated with weight loss management after following different nutritional interventions
Metagenomic biomarker . | Lifestyle intervention . | Population and metagenomics . | Main findings . | Reference . |
---|---|---|---|---|
Prevotella:Bacteroides ratio at baseline | Ad libitum New Nordic Diet (NND) high in fiber/whole grain foods versus an Average Danish Diet (ADD) | 62 Danish participants with increased waist circumference | Individuals with high P/B the NND resulted in a 3.15 kg (95% confidence interval (CI): 1.55; 4.76, p < 0.001) larger body fat loss compared with ADD. | [28] |
No differences was observed among individuals with low P/B (0.88 kg [95% CI: −0.61; 2.37, p = 0.25]) | ||||
Duration: 26 weeks | 16S genera-specific quantitative PCR | |||
Conclusion: subjects with high P/B ratio appeared more susceptible to lose body fat on diets high in fiber and whole grain than subjects with a low P/B ratio | ||||
500 kcal/day energy deficit, 30% fat, 52% carbohydrate, and 18% protein in dairy products containing either high (≈1,500 mg calcium/day) versus low (≤600 mg calcium/day) | 52 Danish overweight participants | Individuals with high P/B lost more body weight and body fat compared to individuals with low P/B | [29] | |
Duration: 6 months | 16S rRNA gene sequencing | Conclusion: individuals with a high P/B are more susceptible to weight loss on a diet rich in fiber | ||
8-week weight loss intervention using meal replacement product, followed by an habitual diet plus protein supplements versus maltodextrin (10–15% of total energy) | 31 Danish participants who lost a minimum of 8% or more of their initial body weight | Subjects with high P/B ratio were more susceptible to regain body weight compared with subjects with low P/B ratio, especially when dietary fiber intake was low and glucose metabolism was impaired | [30] | |
Duration: 24 weeks (weight maintenance period) | Conclusion: individuals with a high P/B are more susceptible to body weight regain | |||
12-month lifestyle intervention (PREDIMED-Plus trial) | 372 Spanish overweight and obese individuals | No differences were observed in the P/B ratio | [31] | |
Energy-reduced Mediterranean diet + promotion of PA | 16S rRNA sequencing | |||
Prevotella abundance at baseline | Ad libitum diet rich in whole-grains (228 g/day) and fiber (33 g/day) versus refined-wheat (RW) diet | 46 Danish healthy overweight adults | Healthy, overweight adults with high Prevotella abundances lost more weight than subjects with low Prevotella abundances when consuming a diet rich in WG and fiber ad libitum for 6 weeks | [32] |
Duration: 6 weeks | 16S rRNA sequencing | Conclusion: Prevotella enterotype could be used as a potential biomarker in personalized nutrition for obesity management | ||
12-month lifestyle intervention (PREDIMED-Plus trial) | 372 Spanish overweight and obese individuals (55–75 years) | Prevotella 9 abundance was more associated with higher weight-loss after 12-months of follow-up | [31] | |
Energy-reduced Mediterranean diet + promotion of PA | 16S rRNA sequencing | Conclusion: Prevotella 9 genera could be used as potential target in personalized nutrition for obesity management | ||
Bacteroides abundance at baseline | High-fiber diet consisting of approximately 30 g fiber/day, of which 10.4 g was obtained from arabinoxylan-oligosaccharides (AXOS) | 15 Danish overweight subjects | Bacteroides cellulosilyticus relative abundance was associated with a higher body weight gain | [33] |
Duration: 4 weeks | Shotgun DNA sequencing | Conclusion: this study permitted to uncover the predictive role of Bacteroides species in weight gain during a fiber-based intervention | ||
Low-carbohydrate diet (LCD) with ad libitum energy intake | 26 Chinese overweight or obese participants | Participants with a higher relative abundance of Bacteroidaceae Bacteroides at baseline exhibited a better response to LCD intervention and achieved greater weight loss outcomes | [34] | |
Duration: 12 weeks | 16S rDNA sequencing | Conclusion: the relative abundance of Bacteroidaceae Bacteroides is a positive outcome predictor of individual weight loss after short-term LCD intervention | ||
12-month lifestyle intervention (PREDIMED-Plus trial) | 372 Spanish overweight and obese individuals (55–75 years) | Bacteroides abundance was more associated with higher weight-loss after 12 months of follow-up | [31] | |
Energy-reduced Mediterranean diet + promotion of PA | 16S rRNA sequencing | Conclusion: Bacteroides genera could be used as potential target in personalized nutrition for obesity management | ||
Baseline gut microbiota | Low-energy diet (LEDs) comprising commercially formulated food products (800–1,200 kcal/day; 3.3–5 MJ/day) | 211 Chinese overweight adults with prediabetes | High relative abundance of Clostridium sensu stricto 1, Ruminococcaceae UCG-003 and Parabacteroides at baseline were predictive of increased fat loss (%) during the intervention | [35] |
Lactococcus and an unclassified genus of Peptostreptococcaceae were significantly associated with a good response, while Erysipelotrichaceae UCG-003 was predictive of poor response | ||||
Duration: 8 weeks | ||||
Conclusion: changes in adiposity can be predicted by baseline features of the gut microbiota | ||||
A weight-reduction program, based in a 60–65% carbohydrate, 24–26% protein, and 12–14% fat (as energy percentages) diet, including 400–500 g of vegetables and 100–200 g of fruits of low glycemic index | 83 Chinese participants | Blautia wexlerae (MGS0575) and Bacteroides dorei (MGS0187) were the strongest predictors for weight loss when present in high abundance at baseline R gnavus (MGS0160), B massiliensis (MGS1424), and B finegoldii (MGS0729), whose decrease in abundance favored weight loss | [26] | |
29 overweight | ||||
Duration: 6 months | 43 obese | The increase of A. muciniphila during dieting was significantly associated with weight loss | ||
11 normal weight | Conclusion: baseline features of the gut microbiota can predict the weight loss after a weight-reduction program | |||
20–45 years | ||||
Macronutrient standardized diet | 80 American overweight and obese participants | Less Escherichia/Shigella, Klebsiella, Megasphaera, Sellimonas, and Lactobacillus, and more Collinsella and an unidentified genus of the family Christensenellaceae | [36] | |
Duration: 16 weeks +14 weeks of calories restriction (500 calorie deficit) | Conclusion: baseline microbiome profiles were able to predict which patients lost at least 5% of their body weight | |||
Commercial behavioral coaching program including recommendations from the American Heart Association or American Diabetes Association; comprehensive lifestyle interventions such as those developed for the Diabetes Prevention Program; nutrition recommendations such as those based on the DASH dietary pattern or MIND diet; and exercise recommendations from the American College of Sports Medicine | 15 American individuals with the largest declines in weight (who lost more than 1% of body weight per month); and 10 individuals with the smallest positive weight change values (gained less than 0.1% of their body weight during the lifestyle intervention) | Gram-negative Bacteroidetes were positively associated with weight loss | [37] | |
Baseline gut microbiome functional genes related to cell wall and lipopolysaccharide synthesis were positively associated with weight loss | ||||
Duration: 12 months | Baseline gut microbiome functional genes involved in glycan and protein catabolism, response to stress, peptide antibiotic synthesis, and respiration were associated with weight loss resistance | |||
Conclusion: baseline gut microbiome functional features are associated with future changes in weight following an intervention | ||||
Multidisciplinary weight-loss program (OPTIFAST® 52, Nestlé Inc.) | 16 German adult patients with obesity with or without comorbidities | Metabolic comorbidities are associated with a higher Firmicutes/Bacteroidetes ratio | [38] | |
Successful weight reduction in the obese is accompanied with increased Akkermansia numbers in feces | ||||
Duration: 52 weeks | ||||
Successful weight reduction (relative weight loss above 10% at the end of the intervention) was associated with an enrichment in Alistipes, Pseudoflavonifractor and enzymes of the oxidative phosphorylation pathway | ||||
Comprehensive lifestyle intervention program (volumetric diet and PA) | 26 American overweight/obese adults | Increased abundance of Phascolarctobacterium was associated with a successful loss of at least 5% of baseline weight after 3 months | [39] | |
Increased abundance of Dialister and of genes encoding gut microbial carbohydrate-active enzymes was associated with a failure to lose 5% of body weight after 3 months | ||||
Duration: 12 months | ||||
Conclusion: a gut microbiota with increased capability for carbohydrate metabolism appears to be associated with decreased weight loss in overweight and obese patients undergoing a lifestyle intervention program | ||||
Weight-reduction program from its Brookings county retail location in South Dakota (USA) | 58 American incoming clients (adults) | Higher baseline OTU-richness as well as differential abundance and/or associations with B. eggerthi, A. muciniphila, Turicibacter, Prevotella, and Christensenella exhibited a higher response to weight loss (losing an average of 14.5 kg) compared to 5.9 kg in the remaining low-response group | [40] | |
Duration: 12 weeks | Conclusion: gut microbiota differences may contribute to variable weight-loss response | |||
European cross-sectional FoCus cohort | 55 obese patients | Parasutterella was significantly associated with BMI and type-2 diabetes and was reduced during weight loss intervention | [41] | |
Dietary intervention consisting in a very low-calorie formula diet for the duration of 12 weeks, followed by a stabilization period of another 14 weeks | ||||
16S rRNA gene sequencing | ||||
4-weeks of hypocaloric balanced diet followed by 12-week multiphase-modified improved ketogenic diet (MDP-i-KD) | 13 individuals with BMI ≥28 kg/m2 | The presence of Parabacteroides distasonis facilitated the weight loss. On the contrary, Ruminococcus torques and Blautia obeum were significantly positively correlated with BMI | [42] | |
Age: 18–65 years | ||||
Duration: at least 4 weeks | Conclusion: Parabacteroides distasonis, Blautia obeum, and Ruminococcus torques could be key targets for gut microbiota-based obesity interventions | |||
Shotgun metagenomics sequencing | ||||
Hypocaloric balanced diet (HBD) | 43 obese people | The abundance of the genera Blautia, Ruminococcus (R. torques and R. gnavus), Lachnoclostridium, Terrisporobacter and Pseudomonas were significantly reduced in those individuals with worse response to the body weight change | [43] | |
16S rRNA sequencing | On the contrary, individuals with high Parabacteroides genus exhibited effective body weight change | |||
Physical activity (PA) effect over body weight response | Shotgut sequencing | Higher abundance of Alistipes putredinis may strengthen the beneficial association of PA for the body weight change | [44] | |
Bifidobacterium abundance | High-protein, low-carbohydrate diet for weight loss | 20 participants with BMI of 33.5 kg/m2 | BMI, body fat mass, and waist circumference reduction over the time of the intervention were associated with a decrease in the Bifidobacterium abundance in all groups | [45] |
16S rRNA sequencing | ||||
Low-carbohydrate high-fat weight reduction diet | 26 overweight/obese individuals (BMI >28 kg/m2) | Bifidobacteria was reduced after weight loss intervention | [46] | |
Duration: 4 weeks | 16S rRNA sequencing |
Metagenomic biomarker . | Lifestyle intervention . | Population and metagenomics . | Main findings . | Reference . |
---|---|---|---|---|
Prevotella:Bacteroides ratio at baseline | Ad libitum New Nordic Diet (NND) high in fiber/whole grain foods versus an Average Danish Diet (ADD) | 62 Danish participants with increased waist circumference | Individuals with high P/B the NND resulted in a 3.15 kg (95% confidence interval (CI): 1.55; 4.76, p < 0.001) larger body fat loss compared with ADD. | [28] |
No differences was observed among individuals with low P/B (0.88 kg [95% CI: −0.61; 2.37, p = 0.25]) | ||||
Duration: 26 weeks | 16S genera-specific quantitative PCR | |||
Conclusion: subjects with high P/B ratio appeared more susceptible to lose body fat on diets high in fiber and whole grain than subjects with a low P/B ratio | ||||
500 kcal/day energy deficit, 30% fat, 52% carbohydrate, and 18% protein in dairy products containing either high (≈1,500 mg calcium/day) versus low (≤600 mg calcium/day) | 52 Danish overweight participants | Individuals with high P/B lost more body weight and body fat compared to individuals with low P/B | [29] | |
Duration: 6 months | 16S rRNA gene sequencing | Conclusion: individuals with a high P/B are more susceptible to weight loss on a diet rich in fiber | ||
8-week weight loss intervention using meal replacement product, followed by an habitual diet plus protein supplements versus maltodextrin (10–15% of total energy) | 31 Danish participants who lost a minimum of 8% or more of their initial body weight | Subjects with high P/B ratio were more susceptible to regain body weight compared with subjects with low P/B ratio, especially when dietary fiber intake was low and glucose metabolism was impaired | [30] | |
Duration: 24 weeks (weight maintenance period) | Conclusion: individuals with a high P/B are more susceptible to body weight regain | |||
12-month lifestyle intervention (PREDIMED-Plus trial) | 372 Spanish overweight and obese individuals | No differences were observed in the P/B ratio | [31] | |
Energy-reduced Mediterranean diet + promotion of PA | 16S rRNA sequencing | |||
Prevotella abundance at baseline | Ad libitum diet rich in whole-grains (228 g/day) and fiber (33 g/day) versus refined-wheat (RW) diet | 46 Danish healthy overweight adults | Healthy, overweight adults with high Prevotella abundances lost more weight than subjects with low Prevotella abundances when consuming a diet rich in WG and fiber ad libitum for 6 weeks | [32] |
Duration: 6 weeks | 16S rRNA sequencing | Conclusion: Prevotella enterotype could be used as a potential biomarker in personalized nutrition for obesity management | ||
12-month lifestyle intervention (PREDIMED-Plus trial) | 372 Spanish overweight and obese individuals (55–75 years) | Prevotella 9 abundance was more associated with higher weight-loss after 12-months of follow-up | [31] | |
Energy-reduced Mediterranean diet + promotion of PA | 16S rRNA sequencing | Conclusion: Prevotella 9 genera could be used as potential target in personalized nutrition for obesity management | ||
Bacteroides abundance at baseline | High-fiber diet consisting of approximately 30 g fiber/day, of which 10.4 g was obtained from arabinoxylan-oligosaccharides (AXOS) | 15 Danish overweight subjects | Bacteroides cellulosilyticus relative abundance was associated with a higher body weight gain | [33] |
Duration: 4 weeks | Shotgun DNA sequencing | Conclusion: this study permitted to uncover the predictive role of Bacteroides species in weight gain during a fiber-based intervention | ||
Low-carbohydrate diet (LCD) with ad libitum energy intake | 26 Chinese overweight or obese participants | Participants with a higher relative abundance of Bacteroidaceae Bacteroides at baseline exhibited a better response to LCD intervention and achieved greater weight loss outcomes | [34] | |
Duration: 12 weeks | 16S rDNA sequencing | Conclusion: the relative abundance of Bacteroidaceae Bacteroides is a positive outcome predictor of individual weight loss after short-term LCD intervention | ||
12-month lifestyle intervention (PREDIMED-Plus trial) | 372 Spanish overweight and obese individuals (55–75 years) | Bacteroides abundance was more associated with higher weight-loss after 12 months of follow-up | [31] | |
Energy-reduced Mediterranean diet + promotion of PA | 16S rRNA sequencing | Conclusion: Bacteroides genera could be used as potential target in personalized nutrition for obesity management | ||
Baseline gut microbiota | Low-energy diet (LEDs) comprising commercially formulated food products (800–1,200 kcal/day; 3.3–5 MJ/day) | 211 Chinese overweight adults with prediabetes | High relative abundance of Clostridium sensu stricto 1, Ruminococcaceae UCG-003 and Parabacteroides at baseline were predictive of increased fat loss (%) during the intervention | [35] |
Lactococcus and an unclassified genus of Peptostreptococcaceae were significantly associated with a good response, while Erysipelotrichaceae UCG-003 was predictive of poor response | ||||
Duration: 8 weeks | ||||
Conclusion: changes in adiposity can be predicted by baseline features of the gut microbiota | ||||
A weight-reduction program, based in a 60–65% carbohydrate, 24–26% protein, and 12–14% fat (as energy percentages) diet, including 400–500 g of vegetables and 100–200 g of fruits of low glycemic index | 83 Chinese participants | Blautia wexlerae (MGS0575) and Bacteroides dorei (MGS0187) were the strongest predictors for weight loss when present in high abundance at baseline R gnavus (MGS0160), B massiliensis (MGS1424), and B finegoldii (MGS0729), whose decrease in abundance favored weight loss | [26] | |
29 overweight | ||||
Duration: 6 months | 43 obese | The increase of A. muciniphila during dieting was significantly associated with weight loss | ||
11 normal weight | Conclusion: baseline features of the gut microbiota can predict the weight loss after a weight-reduction program | |||
20–45 years | ||||
Macronutrient standardized diet | 80 American overweight and obese participants | Less Escherichia/Shigella, Klebsiella, Megasphaera, Sellimonas, and Lactobacillus, and more Collinsella and an unidentified genus of the family Christensenellaceae | [36] | |
Duration: 16 weeks +14 weeks of calories restriction (500 calorie deficit) | Conclusion: baseline microbiome profiles were able to predict which patients lost at least 5% of their body weight | |||
Commercial behavioral coaching program including recommendations from the American Heart Association or American Diabetes Association; comprehensive lifestyle interventions such as those developed for the Diabetes Prevention Program; nutrition recommendations such as those based on the DASH dietary pattern or MIND diet; and exercise recommendations from the American College of Sports Medicine | 15 American individuals with the largest declines in weight (who lost more than 1% of body weight per month); and 10 individuals with the smallest positive weight change values (gained less than 0.1% of their body weight during the lifestyle intervention) | Gram-negative Bacteroidetes were positively associated with weight loss | [37] | |
Baseline gut microbiome functional genes related to cell wall and lipopolysaccharide synthesis were positively associated with weight loss | ||||
Duration: 12 months | Baseline gut microbiome functional genes involved in glycan and protein catabolism, response to stress, peptide antibiotic synthesis, and respiration were associated with weight loss resistance | |||
Conclusion: baseline gut microbiome functional features are associated with future changes in weight following an intervention | ||||
Multidisciplinary weight-loss program (OPTIFAST® 52, Nestlé Inc.) | 16 German adult patients with obesity with or without comorbidities | Metabolic comorbidities are associated with a higher Firmicutes/Bacteroidetes ratio | [38] | |
Successful weight reduction in the obese is accompanied with increased Akkermansia numbers in feces | ||||
Duration: 52 weeks | ||||
Successful weight reduction (relative weight loss above 10% at the end of the intervention) was associated with an enrichment in Alistipes, Pseudoflavonifractor and enzymes of the oxidative phosphorylation pathway | ||||
Comprehensive lifestyle intervention program (volumetric diet and PA) | 26 American overweight/obese adults | Increased abundance of Phascolarctobacterium was associated with a successful loss of at least 5% of baseline weight after 3 months | [39] | |
Increased abundance of Dialister and of genes encoding gut microbial carbohydrate-active enzymes was associated with a failure to lose 5% of body weight after 3 months | ||||
Duration: 12 months | ||||
Conclusion: a gut microbiota with increased capability for carbohydrate metabolism appears to be associated with decreased weight loss in overweight and obese patients undergoing a lifestyle intervention program | ||||
Weight-reduction program from its Brookings county retail location in South Dakota (USA) | 58 American incoming clients (adults) | Higher baseline OTU-richness as well as differential abundance and/or associations with B. eggerthi, A. muciniphila, Turicibacter, Prevotella, and Christensenella exhibited a higher response to weight loss (losing an average of 14.5 kg) compared to 5.9 kg in the remaining low-response group | [40] | |
Duration: 12 weeks | Conclusion: gut microbiota differences may contribute to variable weight-loss response | |||
European cross-sectional FoCus cohort | 55 obese patients | Parasutterella was significantly associated with BMI and type-2 diabetes and was reduced during weight loss intervention | [41] | |
Dietary intervention consisting in a very low-calorie formula diet for the duration of 12 weeks, followed by a stabilization period of another 14 weeks | ||||
16S rRNA gene sequencing | ||||
4-weeks of hypocaloric balanced diet followed by 12-week multiphase-modified improved ketogenic diet (MDP-i-KD) | 13 individuals with BMI ≥28 kg/m2 | The presence of Parabacteroides distasonis facilitated the weight loss. On the contrary, Ruminococcus torques and Blautia obeum were significantly positively correlated with BMI | [42] | |
Age: 18–65 years | ||||
Duration: at least 4 weeks | Conclusion: Parabacteroides distasonis, Blautia obeum, and Ruminococcus torques could be key targets for gut microbiota-based obesity interventions | |||
Shotgun metagenomics sequencing | ||||
Hypocaloric balanced diet (HBD) | 43 obese people | The abundance of the genera Blautia, Ruminococcus (R. torques and R. gnavus), Lachnoclostridium, Terrisporobacter and Pseudomonas were significantly reduced in those individuals with worse response to the body weight change | [43] | |
16S rRNA sequencing | On the contrary, individuals with high Parabacteroides genus exhibited effective body weight change | |||
Physical activity (PA) effect over body weight response | Shotgut sequencing | Higher abundance of Alistipes putredinis may strengthen the beneficial association of PA for the body weight change | [44] | |
Bifidobacterium abundance | High-protein, low-carbohydrate diet for weight loss | 20 participants with BMI of 33.5 kg/m2 | BMI, body fat mass, and waist circumference reduction over the time of the intervention were associated with a decrease in the Bifidobacterium abundance in all groups | [45] |
16S rRNA sequencing | ||||
Low-carbohydrate high-fat weight reduction diet | 26 overweight/obese individuals (BMI >28 kg/m2) | Bifidobacteria was reduced after weight loss intervention | [46] | |
Duration: 4 weeks | 16S rRNA sequencing |
NND, Nordic diet; ADD, Average Danish Diet; AXOS, arabinoxylan-oligosaccharides; RW, refined-wheat; LEDs, low-energy diet; LCD, low-carbohydrate diet; P/B, Prevotella-to-Bacteroides ratio.
As can be seen, several studies have determined some genera, families, or bacterial species with a greater or lesser predisposition to weight loss. However, the different methodology of analyses of the intestinal microbiota (16S sequencing in most cases, less frequent shotgun), as well as the different interventions performed make it difficult and limit obtaining conclusive data. However, despite the differences in the populations, we found some similarities between studies. This is the case of Bacteroides thetaiotaomicron, species that was significantly associated with body weight reduction after sleeve gastrectomy in obese individuals [47]. A similar finding was also observed in another study, where B. thetaiotaomicron, B. nordii, and B. uniformis were negatively correlated with BMI after bariatric surgery [48], showing a similar pattern in Bacteroides after this type of surgical intervention. Moreover, the genera Bacteroides was also correlated with a higher weight loss after a 12-month lifestyle intervention [31]. On the contrary, Bifidobacterium genera seem to be reduced after body weight reduction [45].
Another limitation that some of the studies have is that they do not differentiate, in many cases, between men and women. In this sense, it is worth mentioning the study carried out by Cuevas-Sierra and colleagues [49], which developed an integrative model based on microbiota and genetic information that would allow to prescribe the most suitable diet for a successful weight loss intervention, differentiating by sex.
Gut microbiota profiles have also been associated with metabolic outcomes after following specific dietary treatments. For example, individuals with a high Prevotella-to-Bacteroides ratio showed better improvements in glucose metabolism (responders) following 3-day consumption of barley kernel-based bread compared to those who responded least (nonresponders) to this dietary intervention. Interestingly, bacterial profiling analysis revealed that the gut microbiota of responders was enriched in Prevotella copri, with an increased potential to ferment complex polysaccharides after high fiber consumption [50]. Also, the beneficial effect of dietary capsaicin on metabolic and immune profiles (including increased plasma levels of glucagon-like peptide 1 and gastric inhibitory polypeptide and decreased plasma ghrelin level) was accompanied by increased Firmicutes/Bacteroidetes ratio and Faecalibacterium abundance in healthy subjects [51]. Likewise, higher total plasma cholesterol levels were found in individuals with high Prevotella-to-Bacteroides ratio after the consumption of a new Nordic diet during 6 months [52]. Finally, Lactobacillus abundance was negatively correlated with body fat mass and was associated with a reduction in blood glucose and HbA1C parameters after a high-protein, low-carbohydrate diet [45].
The importance of the gut microbiota signature has also been related with the beneficial effect of PA over body weight control. Thus, a recent study performed in healthy individuals in middle-to-late adulthood demonstrated that those subjects with high prevalence of Alistipes putredinis had stronger association of PA with reduced body weight gain and reduced levels of obesity-related plasma biomarkers, compared with those with low prevalence of this species [44].
Finally, we shall mention that Table 1 has only attempted to reflect the changes in the microbiota associated with physiological weight loss, as a result of a nutritional intervention. Pharmacological interventions described for weight loss, such as the use of Orlistat or GLP-1 agonists, were excluded due to the demonstrated role of these drugs on the intestinal microbial composition per se [53], which may mask the results observed as a consequence of weight loss.
Integrating Gut Bacterial Profiling with Other Omics Technologies
In the last years, the combination of multiple omics tools using systems biology has emerged as an innovative holistic scope to provide a more comprehensive understanding of the molecular and physiological events underlying human diseases (including obesity) and therapy outcomes [55]. In this context, a crosstalk between gut microbiota composition and epigenetic biomarkers in obesity status has been reported, where mediation tests showed that relationship between abundance of the Ruminococcus genus and BMI is partially mediated by the methylation status of the MACROD2/SEL1L2 genes [56]. In addition, an interaction between the abundance of 4 bacterial species (i.e., Bacteroides eggerthi) and 14 circulating microRNAs (i.e., miR-130b-3p, miR-185-5p and miR-21-5p) in relation to obesity has been evidenced [57]. Moreover, it has been described that interrelationships between Prevotellaceae abundance and an obesity-related genetic risk score may determine interindividual BMI differences in women [58]. Remarkably, a weight-loss model based on baseline microbiota features and genetic scores was fitted for selection of personalized dietary treatments varying in macronutrient distribution for the precision treatment obesity [49]. Furthermore, obesity-associated gut microbial species linked to changes in circulating metabolites have been identified. For example, the abundance of Bacteroides thetaiotaomicron (a glutamate-fermenting commensal) was markedly decreased in Chinese individuals with obesity, was inversely correlated with serum glutamate concentration, and was found to be significantly associated with body weight reduction [47].
Conclusions
Gut microbiota status is implicated in health maintenance and disease risk. The application of microbiota information can be of great value for precision nutrition in obesity since it contributes to the understanding of the role of the gut microbiota in obesity development, facilitates the identification of potential microorganism targets, and allows the personalization of weight loss diets as well as the prediction of adiposity outcomes. The integration of this information with genetic, epigenetic or metabolomic data can open the door to even more accurate predictions.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
Authors acknowledge the funding obtained from CIBERobn (Grant No. CB12/03/30002) and Ministerio de Economía y Competitividad of Spain (Grant No. PID2022-141766OB-I00 and RTI2018-102205-B-I00) to study the gut metagenome and its mechanisms of action.
Author Contributions
O.R.-L. and F.I.M. wrote the manuscript. J.I.R.-B. and P.A. searched bibliography and critically reviewed the document. All authors approved the final version of the manuscript.