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BACKGROUND: Persons living with HIV (PWH) harbor an altered gut microbiome (higher abundance of Prevotella and lower abundance of Bacillota and Ruminococcus lineages) compared to non-infected individuals. Some of these alterations are linked to sexual preference and others to the HIV infection. The relationship between these lineages and metabolic alterations, often present in aging PWH, has been poorly investigated. METHODS: In this study, we compared fecal metagenomes of 25 antiretroviral-treatment (ART)-controlled PWH to three independent control groups of 25 non-infected matched individuals by means of univariate analyses and machine learning methods. Moreover, we used two external datasets to validate predictive models of PWH classification. Next, we searched for associations between clinical and biological metabolic parameters with taxonomic and functional microbiome profiles. Finally, we compare the gut microbiome in 7 PWH after a 17-week ART switch to raltegravir/maraviroc. RESULTS: Three major enterotypes (Prevotella, Bacteroides and Ruminococcaceae) were present in all groups. The first Prevotella enterotype was enriched in PWH, with several of characteristic lineages associated with poor metabolic profiles (low HDL and adiponectin, high insulin resistance (HOMA-IR)). Conversely butyrate-producing lineages were markedly depleted in PWH independently of sexual preference and were associated with a better metabolic profile (higher HDL and adiponectin and lower HOMA-IR). Accordingly with the worst metabolic status of PWH, butyrate production and amino-acid degradation modules were associated with high HDL and adiponectin and low HOMA-IR. Random Forest models trained to classify PWH vs. control on taxonomic abundances displayed high generalization performance on two external holdout datasets (ROC AUC of 80-82%). Finally, no significant alterations in microbiome composition were observed after switching to raltegravir/maraviroc. CONCLUSION: High resolution metagenomic analyses revealed major differences in the gut microbiome of ART-controlled PWH when compared with three independent matched cohorts of controls. The observed marked insulin resistance could result both from enrichment in Prevotella lineages, and from the depletion in species producing butyrate and involved into amino-acid degradation, which depletion is linked with the HIV infection.
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Microbioma Gastrointestinal , Infecciones por VIH , Resistencia a la Insulina , Humanos , Microbioma Gastrointestinal/efectos de los fármacos , Infecciones por VIH/tratamiento farmacológico , Infecciones por VIH/microbiología , Masculino , Femenino , Persona de Mediana Edad , Adulto , Heces/microbiología , Antirretrovirales/uso terapéutico , MetagenomaRESUMEN
Amino acids, metabolized by host cells as well as commensal gut bacteria, have signaling effects on host metabolism. Oral supplementation of the essential amino acid histidine has been shown to exert metabolic benefits. To investigate whether dietary histidine aids glycemic control, we performed a case-controlled parallel clinical intervention study in participants with type 2 diabetes (T2D) and healthy controls. Participants received oral histidine for seven weeks. After 2 weeks of histidine supplementation, the microbiome was depleted by antibiotics to determine the microbial contribution to histidine metabolism. We assessed glycemic control, immunophenotyping of peripheral blood mononucelar cells (PBMC), DNA methylation of PBMCs and fecal gut microbiota composition. Histidine improves several markers of glycemic control, including postprandial glucose levels with a concordant increase in the proportion of MAIT cells after two weeks of histidine supplementation. The increase in MAIT cells was associated with changes in gut microbial pathways such as riboflavin biosynthesis and epigenetic changes in the amino acid transporter SLC7A5. Associations between the microbiome and MAIT cells were replicated in the MetaCardis cohort. We propose a conceptual framework for how oral histidine may affect MAIT cells via altered gut microbiota composition and SLC7A5 expression in MAIT cells directly and thereby influencing glycemic control. Future studies should focus on the role of flavin biosynthesis intermediates and SLC7A5 modulation in MAIT cells to modulate glycemic control.
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Diabetes Mellitus Tipo 2 , Microbioma Gastrointestinal , Histidina , Células T Invariantes Asociadas a Mucosa , Humanos , Histidina/metabolismo , Diabetes Mellitus Tipo 2/microbiología , Diabetes Mellitus Tipo 2/metabolismo , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Diabetes Mellitus Tipo 2/inmunología , Microbioma Gastrointestinal/efectos de los fármacos , Persona de Mediana Edad , Masculino , Femenino , Células T Invariantes Asociadas a Mucosa/inmunología , Células T Invariantes Asociadas a Mucosa/metabolismo , Control Glucémico , Suplementos Dietéticos , Estudios de Casos y Controles , Heces/microbiología , Glucemia/metabolismo , Anciano , Adulto , Bacterias/clasificación , Bacterias/metabolismo , Bacterias/genética , Administración Oral , Metilación de ADNRESUMEN
The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.
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Aprendizaje Profundo , Microbioma Gastrointestinal , Microbiota , Humanos , Metagenoma , Metagenómica/métodosRESUMEN
The gut microbiome plays a significant role in the development of Type 2 Diabetes Mellitus (T2DM), but the functional mechanisms behind this association merit deeper investigation. Here, we used the nanopore sequencing technology for metagenomic analyses to compare the gut microbiome of individuals with T2DM from the United Arab Emirates (n = 40) with that of control (n = 44). DMM enterotyping of the cohort resulted concordantly with previous results, in three dominant groups Bacteroides (K1), Firmicutes (K2), and Prevotella (K3) lineages. The diversity analysis revealed a high level of diversity in the Firmicutes group (K2) both in terms of species richness and evenness (Wilcoxon rank-sum test, p value < 0.05 vs. K1 and K3 groups), consistent with the Ruminococcus enterotype described in Western populations. Additionally, functional enrichment analyses of KEGG modules showed significant differences in abundance between individuals with T2DM and controls (FDR < 0.05). These differences include modules associated with the degradation of amino acids, such as arginine, the degradation of urea as well as those associated with homoacetogenesis. Prediction analysis with the Predomics approach suggested potential biomarkers for T2DM, including a balance between a depletion of Enterococcus faecium and Blautia lineages with an enrichment of Absiella spp or Eubacterium limosum in T2DM individuals, highlighting the potential of metagenomic analysis in predicting predisposition to diabetic cardiomyopathy in T2DM patients.
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Diabetes Mellitus Tipo 2 , Cardiomiopatías Diabéticas , Microbioma Gastrointestinal , Humanos , Diabetes Mellitus Tipo 2/metabolismo , Microbioma Gastrointestinal/genética , Firmicutes , MetagenomaRESUMEN
The host-microbiota co-metabolite trimethylamine N-oxide (TMAO) is linked to increased cardiovascular risk but how its circulating levels are regulated remains unclear. We applied "explainable" machine learning, univariate, multivariate and mediation analyses of fasting plasma TMAO concentration and a multitude of phenotypes in 1,741 adult Europeans of the MetaCardis study. Here we show that next to age, kidney function is the primary variable predicting circulating TMAO, with microbiota composition and diet playing minor, albeit significant, roles. Mediation analysis suggests a causal relationship between TMAO and kidney function that we corroborate in preclinical models where TMAO exposure increases kidney scarring. Consistent with our findings, patients receiving glucose-lowering drugs with reno-protective properties have significantly lower circulating TMAO when compared to propensity-score matched control individuals. Our analyses uncover a bidirectional relationship between kidney function and TMAO that can potentially be modified by reno-protective anti-diabetic drugs and suggest a clinically actionable intervention for decreasing TMAO-associated excess cardiovascular risk.
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Endocrinología , Metilaminas , Adulto , Humanos , Causalidad , RiñónRESUMEN
High-protein diets are promoted for individuals with type 2 diabetes (T2D). However, effects of dietary protein interventions on (gut-derived) metabolites in T2D remains understudied. We therefore performed a multi-center, randomized-controlled, isocaloric protein intervention with 151 participants following either 12-week high-protein (HP; 30Energy %, N = 78) vs. low-protein (LP; 10 Energy%, N = 73) diet. Primary objectives were dietary effects on glycemic control which were determined via glycemic excursions, continuous glucose monitors and HbA1c. Secondary objectives were impact of diet on gut microbiota composition and -derived metabolites which were determined by shotgun-metagenomics and mass spectrometry. Analyses were performed using delta changes adjusting for center, baseline, and kidney function when appropriate. This study found that a short-term 12-week isocaloric protein modulation does not affect glycemic parameters or weight in metformin-treated T2D. However, the HP diet slightly worsened kidney function, increased alpha-diversity, and production of potentially harmful microbiota-dependent metabolites, which may affect host metabolism upon prolonged exposure.
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A growing body of evidence suggests interplay between the gut microbiota and the pathogenesis of nonalcoholic fatty liver disease (NAFLD). However, the role of the gut microbiome in early detection of NAFLD is unclear. Prospective studies are necessary for identifying reliable, microbiome markers for early NAFLD. We evaluated 2487 individuals in a community-based cohort who were followed up 4.6 years after initial clinical examination and biospecimen sampling. Metagenomic and metabolomic characterizations using stool and serum samples taken at baseline were performed for 90 participants who progressed to NAFLD and 90 controls who remained NAFLD free at the follow-up visit. Cases and controls were matched for gender, age, body mass index (BMI) at baseline and follow-up, and 4-year BMI change. Machine learning models integrating baseline microbial signatures (14 features) correctly classified participants (auROCs of 0.72 to 0.80) based on their NAFLD status and liver fat accumulation at the 4-year follow up, outperforming other prognostic clinical models (auROCs of 0.58 to 0.60). We confirmed the biological relevance of the microbiome features by testing their diagnostic ability in four external NAFLD case-control cohorts examined by biopsy or magnetic resonance spectroscopy, from Asia, Europe, and the United States. Our findings raise the possibility of using gut microbiota for early clinical warning of NAFLD development.
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Microbioma Gastrointestinal , Enfermedad del Hígado Graso no Alcohólico , Biomarcadores , Humanos , Enfermedad del Hígado Graso no Alcohólico/patología , Estudios Prospectivos , Medición de RiesgoRESUMEN
Roux-en-Y gastric bypass (RYGB) is efficient at inducing drastic albeit variable weight loss and type-2 diabetes (T2D) improvements in patients with severe obesity and T2D. We hypothesized a causal implication of the gut microbiota (GM) in these metabolic benefits, as RYGB is known to deeply impact its composition. In a cohort of 100 patients with baseline T2D who underwent RYGB and were followed for 5-years, we used a hierarchical clustering approach to stratify subjects based on the severity of their T2D (Severe vs Mild) throughout the follow-up. We identified via nanopore-based GM sequencing that the more severe cases of unresolved T2D were associated with a major increase of the class Bacteroidia, including 12 species comprising Phocaeicola dorei, Bacteroides fragilis, and Bacteroides caecimuris. A key observation is that patients who underwent major metabolic improvements do not harbor this enrichment in Bacteroidia, as those who presented mild cases of T2D at all times. In a separate group of 36 patients with similar baseline clinical characteristics and preoperative GM sequencing, we showed that this increase in Bacteroidia was already present at baseline in the most severe cases of T2D. To explore the causal relationship linking this enrichment in Bacteroidia and metabolic alterations, we selected 13 patients across T2D severity clusters at 5-years and performed fecal matter transplants in mice. Our results show that 14 weeks after the transplantations, mice colonized with the GM of Severe donors have impaired glucose tolerance and insulin sensitivity as compared to Mild-recipients, all in the absence of any difference in body weight and composition. GM sequencing of the recipient animals revealed that the hallmark T2D-severity associated bacterial features were transferred and were associated with the animals' metabolic alterations. Therefore, our results further establish the GM as a key contributor to long-term glucose metabolism improvements (or lack thereof) after RYGB.
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Diabetes Mellitus Tipo 2 , Derivación Gástrica , Microbioma Gastrointestinal , Animales , Bacteroidetes , Peso Corporal , Diabetes Mellitus Tipo 2/microbiología , Derivación Gástrica/métodos , Humanos , Ratones , Pérdida de PesoRESUMEN
Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages-acute coronary syndrome, chronic IHD and IHD with heart failure-and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.
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Enfermedades Cardiovasculares , Diabetes Mellitus Tipo 2 , Microbiota , Humanos , Estudios Longitudinales , Metaboloma , Persona de Mediana EdadRESUMEN
OBJECTIVES: Gut microbiota is a key component in obesity and type 2 diabetes, yet mechanisms and metabolites central to this interaction remain unclear. We examined the human gut microbiome's functional composition in healthy metabolic state and the most severe states of obesity and type 2 diabetes within the MetaCardis cohort. We focused on the role of B vitamins and B7/B8 biotin for regulation of host metabolic state, as these vitamins influence both microbial function and host metabolism and inflammation. DESIGN: We performed metagenomic analyses in 1545 subjects from the MetaCardis cohorts and different murine experiments, including germ-free and antibiotic treated animals, faecal microbiota transfer, bariatric surgery and supplementation with biotin and prebiotics in mice. RESULTS: Severe obesity is associated with an absolute deficiency in bacterial biotin producers and transporters, whose abundances correlate with host metabolic and inflammatory phenotypes. We found suboptimal circulating biotin levels in severe obesity and altered expression of biotin-associated genes in human adipose tissue. In mice, the absence or depletion of gut microbiota by antibiotics confirmed the microbial contribution to host biotin levels. Bariatric surgery, which improves metabolism and inflammation, associates with increased bacterial biotin producers and improved host systemic biotin in humans and mice. Finally, supplementing high-fat diet-fed mice with fructo-oligosaccharides and biotin improves not only the microbiome diversity, but also the potential of bacterial production of biotin and B vitamins, while limiting weight gain and glycaemic deterioration. CONCLUSION: Strategies combining biotin and prebiotic supplementation could help prevent the deterioration of metabolic states in severe obesity. TRIAL REGISTRATION NUMBER: NCT02059538.
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Diabetes Mellitus Tipo 2 , Microbioma Gastrointestinal , Obesidad Mórbida , Complejo Vitamínico B , Humanos , Ratones , Animales , Prebióticos , Obesidad Mórbida/cirugía , Biotina/farmacología , Complejo Vitamínico B/farmacología , Ratones Endogámicos C57BL , Obesidad/metabolismo , InflamaciónRESUMEN
The gut microbiome plays a major role in chronic diseases, of which several are characterized by an altered composition and diversity of bacterial communities. Large-scale sequencing projects allowed for characterizing the perturbations of these communities. However, translating these discoveries into clinical applications remains a challenge. To facilitate routine implementation of microbiome profiling in clinical settings, portable, real-time, and low-cost sequencing technologies are needed. Here, we propose a computational and experimental protocol for whole-genome semi-quantitative metagenomic studies of human gut microbiome with Oxford Nanopore sequencing technology (ONT) that could be applied to other microbial ecosystems. We developed a bioinformatics protocol to analyze ONT sequences taxonomically and functionally and optimized preanalytic protocols, including stool collection and DNA extraction methods to maximize read length. This is a critical parameter for the sequence alignment and classification. Our protocol was evaluated using simulations of metagenomic communities, which reflect naturally occurring compositional variations. Next, we validated both protocols using stool samples from a bariatric surgery cohort, sequenced with ONT, Illumina, and SOLiD technologies. Results revealed similar diversity and microbial composition profiles. This protocol can be implemented in a clinical or research setting, bringing rapid personalized whole-genome profiling of target microbiome species.
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Metagenómica , Secuenciación de Nanoporos/métodos , Biología Computacional/métodos , Microbioma Gastrointestinal/genética , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , HumanosRESUMEN
BACKGROUND: Protein intake has been associated with the development of pre-diabetes (pre-T2D) and type 2 diabetes (T2D). The gut microbiota has the capacity to produce harmful metabolites derived from dietary protein. Furthermore, both the gut microbiota composition and metabolic status (e.g., insulin resistance) can be modulated by diet and ethnicity. However, to date most studies have predominantly focused on carbohydrate and fiber intake with regards to metabolic status and gut microbiota composition. OBJECTIVES: To determine the associations between dietary protein intake, gut microbiota composition, and metabolic status in different ethnicities. METHODS: Separate cross-sectional analysis of two European cohorts (MetaCardis, n = 1759; HELIUS, n = 1528) including controls, patients with pre-T2D, and patients with T2D of Caucasian/non-Caucasian origin with nutritional data obtained from Food Frequency Questionnaires and gut microbiota composition. RESULTS: In both cohorts, animal (but not plant) protein intake was associated with pre-T2D status and T2D status after adjustment for confounders. There was no significant association between protein intake (total, animal, or plant) with either gut microbiota alpha diversity or beta diversity, regardless of ethnicity. At the species level, we identified taxonomical signatures associated with animal protein intake that overlapped in both cohorts with different abundances according to metabolic status and ethnicity. CONCLUSIONS: Animal protein intake is associated with pre-T2D and T2D status but not with gut microbiota beta or alpha diversity, regardless of ethnicity. Gut microbial taxonomical signatures were identified, which could function as potential modulators in the association between dietary protein intake and metabolic status.
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Proteínas en la Dieta/farmacología , Metabolismo Energético , Microbioma Gastrointestinal/efectos de los fármacos , Grupos Raciales , Adulto , Anciano , ADN Bacteriano/genética , Dieta , Etnicidad , Heces/microbiología , Femenino , Humanos , Masculino , Persona de Mediana EdadRESUMEN
The advent of high-throughput sequencing techniques has recently provided an astonishing insight into the composition and function of the human microbiome. Next-generation sequencing (NGS) has become the gold standard for advanced microbiome analysis; however, 3rd generation real-time sequencing, such as Oxford Nanopore Technologies (ONT), enables rapid sequencing from several kilobases to >2 Mb with high resolution. Despite the wide availability and the enormous potential for clinical and translational applications, ONT is poorly standardized in terms of sampling and storage conditions, DNA extraction, library creation, and bioinformatic classification. Here, we present a comprehensive analysis pipeline with sampling, storage, DNA extraction, library preparation, and bioinformatic evaluation for complex microbiomes sequenced with ONT. Our findings from buccal and rectal swabs and DNA extraction experiments indicate that methods that were approved for NGS microbiome analysis cannot be simply adapted to ONT. We recommend using swabs and DNA extractions protocols with extended washing steps. Both 16S rRNA and metagenomic sequencing achieved reliable and reproducible results. Our benchmarking experiments reveal thresholds for analysis parameters that achieved excellent precision, recall, and area under the precision recall values and is superior to existing classifiers (Kraken2, Kaiju, and MetaMaps). Hence, our workflow provides an experimental and bioinformatic pipeline to perform a highly accurate analysis of complex microbial structures from buccal and rectal swabs. IMPORTANCE Advanced microbiome analysis relies on sequencing of short DNA fragments from microorganisms like bacteria, fungi, and viruses. More recently, long fragment DNA sequencing of 3rd generation sequencing has gained increasing importance and can be rapidly conducted within a few hours due to its potential real-time sequencing. However, the analysis and correct identification of the microbiome relies on a multitude of factors, such as the method of sampling, DNA extraction, sequencing, and bioinformatic analysis. Scientists have used different protocols in the past that do not allow us to compare results across different studies and research fields. Here, we provide a comprehensive workflow from DNA extraction, sequencing, and bioinformatic workflow that allows rapid and accurate analysis of human buccal and rectal swabs with reproducible protocols. This workflow can be readily applied by many scientists from various research fields that aim to use long-fragment microbiome sequencing.
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Interactions between diet and gut microbiota are critical regulators of energy metabolism. The effects of fibre intake have been deeply studied but little is known about the impact of proteins. Here, we investigated the effects of high protein supplementation (Investigational Product, IP) in a double blind, randomised placebo-controled intervention study (NCT01755104) where 107 participants received the IP or an isocaloric normoproteic comparator (CP) alongside a mild caloric restriction. Gut microbiota profiles were explored in a patient subset (n = 53) using shotgun metagenomic sequencing. Visceral fat decreased in both groups (IP group: - 20.8 ± 23.2 cm2; CP group: - 14.5 ± 24.3 cm2) with a greater reduction (p < 0.05) with the IP supplementation in the Per Protocol population. Microbial diversity increased in individuals with a baseline low gene count (p < 0.05). The decrease in weight, fat mass and visceral fat mass significantly correlated with the increase in microbial diversity (p < 0.05). Protein supplementation had little effects on bacteria composition but major differences were seen at functional level. Protein supplementation stimulated bacterial amino acid metabolism (90% amino-acid synthesis functions enriched with IP versus 13% in CP group (p < 0.01)). Protein supplementation alongside a mild energy restriction induces visceral fat mass loss and an activation of gut microbiota amino-acid metabolism.Clinical trial registration: NCT01755104 (24/12/2012). https://clinicaltrials.gov/ct2/show/record/NCT01755104?term=NCT01755104&draw=2&rank=1 .
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Restricción Calórica , Microbioma Gastrointestinal , Metagenómica , Adulto , Método Doble Ciego , Humanos , Grasa Intraabdominal , Masculino , Pérdida de PesoRESUMEN
BACKGROUND: Roux-en-Y gastric bypass (RYGB) surgery is one of the most efficient procedures for the treatment of obesity, also improving metabolic and inflammatory status, in patients with mild obesity. The underlying mechanisms have not been fully understood, but gut microbiota is hypothesized to play a key role. Our aim was to evaluate the association between gut microbiota changes and anthropometric, metabolic and inflammatory profiles after metabolic surgery compared with medical therapy, in type 2 diabetic (T2DM) adults with mild obesity (BMI 30-35 kg/m2). METHODS: DM2 was an open-label, randomised controlled clinical trial (RCT: ISRCTN53984585) with 2 arms: (i) surgical, and (ii) medical. The main outcome was gut microbiota changes after: metabolic surgery (Roux-en-Y gastric bypass-RYGB) versus standard medical therapy. Secondary outcomes included anthropometric, metabolic and inflammatory profiles. Clinical visits, blood workup, and stool samples were collected at baseline and months (M)1, 3, 6, 12. Gut microbiota was profiled using 16S rRNA targeted sequencing. RESULTS: Twenty patients were included: 10 in surgical and 10 in medical arm. Anthropometric and metabolic comparative analysis favoured RYGB over medical arm. At M12, the percentage of weight loss was 25.5 vs. 4.9% (p < 0.001) and HbA1c was 6.2 vs. 7.7% (p < 0.001) respectively. We observed a continuous increase of genus richness after RYGB up until M12. In the medical arm, genus richness ended-up being significantly lower at M12. Composition analysis indicated significant changes of the overall microbial ecosystem (permanova p = 0.004, [R2 = 0.17]) during the follow-up period after RYGB. There was a strong association between improvement of anthropometric/metabolic/inflammatory biomarkers and increase in microbial richness and Proteobacterial lineages. CONCLUSIONS: This was the first RCT studying composite clinical, analytic, and microbiome changes in T2DM patients with class 1 obesity after RYGB versus standard medical therapy. The remarkable phenotypic improvement after surgery occurred concomitantly with changes in the gut microbiome, but at a lower level. TRIAL REGISTRATION: ISRCTN53984585.
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BACKGROUND: Dietary intervention is a cornerstone of weight loss therapies. In obesity, a dysbiotic gut microbiota (GM) is characterized by high levels of Bacteroides lineages and low diversity. We examined the GM composition changes, including the Bacteroides 2 enterotype (Bact2), in a real-world weight loss study in subjects following a high-protein hypocaloric diet with or without a live microorganisms (LMP) supplement. METHOD: 263 volunteers were part of this real-world weight loss program. The first phase was a high-protein low-carbohydrate calorie restriction diet with or without LMP supplements. Fecal samples were obtained at baseline and after 10% weight loss for 163 subjects. Metagenomic profiling was obtained by shotgun sequencing. RESULTS: At baseline, the Bact2 enterotype was more prevalent in subjects with aggravated obesity and metabolic alterations. After weight loss, diversity increased and Bact2 prevalence decreased in subjects with lower GM diversity at baseline, notably in LMP consumers. Significant increases in Akkermansia muciniphila and Parabacteroides distasonis and significant decreases of Eubacterium rectale, Streptococcus thermophilus and Bifidobacterial lineages were observed after weight loss. CONCLUSIONS: Baseline microbiome composition is associated with differential changes in GM diversity and Bact2 enterotype prevalence after weight loss. Examining these signatures could drive future personalized nutrition efforts towards more favorable microbiome compositions.
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Microbiota-host-diet interactions contribute to the development of metabolic diseases. Imidazole propionate is a novel microbially produced metabolite from histidine, which impairs glucose metabolism. Here, we show that subjects with prediabetes and diabetes in the MetaCardis cohort from three European countries have elevated serum imidazole propionate levels. Furthermore, imidazole propionate levels were increased in subjects with low bacterial gene richness and Bacteroides 2 enterotype, which have previously been associated with obesity. The Bacteroides 2 enterotype was also associated with increased abundance of the genes involved in imidazole propionate biosynthesis from dietary histidine. Since patients and controls did not differ in their histidine dietary intake, the elevated levels of imidazole propionate in type 2 diabetes likely reflects altered microbial metabolism of histidine, rather than histidine intake per se. Thus the microbiota may contribute to type 2 diabetes by generating imidazole propionate that can modulate host inflammation and metabolism.