The Role of the Gut Microbiome in Predicting Response to Diet and the Development of Precision Nutrition Models-Part I: Overview of Current Methods

Maternal mood, anxiety and mental health functioning after combined myo-inositol, probiotics, micronutrient supplementation from preconception: Findings from the NiPPeR RCT

Observational studies have reported associations between nutrition during pregnancy and mental wellbeing. As secondary outcomes, the NiPPeR double-blind randomized trial in women planning conception investigated whether a myo-inositol, probiotics and enriched micronutrients formulation (intervention) taken preconception and throughout pregnancy could improve mental wellbeing during pregnancy and post-delivery, compared with a standard micronutrient supplement (control). Mood and anxiety symptoms were ascertained (Edinburgh Postnatal Depression Scale (EPDS), State-Trait Anxiety Inventory (STAI-state)) at preconception (baseline), 7, 28 and 34 weeks gestation, 3-weeks and 6-months post-delivery. EPDS>=13 was categorised as low mood; STAI-state>=45 as high anxiety. Change in mental health functioning was assessed as difference between preconception baseline and 6-month post-delivery 12-item Short-Form Health Survey (SF-12v2) mental component scores. Adjusting for site, ethnicity and baseline scores, there were no robust differences in EPDS and STAI-state scores between intervention and control groups across pregnancy (n = 630) and post-delivery (n = 532). Compared to controls, intervention group women averaged a 1.21 (95 %CI 0.04,2.39) higher change in SF-12v2 mental component score from preconception to 6-months post-delivery. Taking a myo-inositol, micronutrient and probiotic supplement during preconception/pregnancy had no effect on mood and anxiety, but there was evidence of a modest improvement in mental health functioning from preconception to 6-months post-delivery.

Human Nutrition Research in the Data Era: Results of 11 Reports on the Effects of a Multiple-Micronutrient-Intervention Study

Large datasets have been used in molecular and genetic research for decades, but only a few studies have included nutrition and lifestyle factors. Our team conducted an n-of-1 intervention with 12 vitamins and five minerals in 9- to 13-year-old Brazilian children and teens with poor healthy-eating indices. A unique feature of the experimental design was the inclusion of a replication arm. Twenty-six types of data were acquired including clinical measures, whole-genome mapping, whole-exome sequencing, and proteomic and a variety of metabolomic measurements over two years. A goal of this study was to use these diverse data sets to discover previously undetected physiological effects associated with a poor diet that include a more complete micronutrient composition. We summarize the key findings of 11 reports from this study that (i) found that LDL and total cholesterol and fasting glucose decreased in the population after the intervention but with inter-individual variation; (ii) associated a polygenic risk score that predicted baseline vitamin B12 levels; (iii) identified metabotypes linking diet intake, genetic makeup, and metabolic physiology; (iv) found multiple biomarkers for nutrient and food groups; and (v) discovered metabolites and proteins that are associated with DNA damage. This summary also highlights the limitations and lessons in analyzing diverse omic data.

Peppermint extract improves egg production and quality, increases antioxidant capacity, and alters cecal microbiota in late-phase laying hens

Introduction

Peppermint contains substantial bioactive ingredients belonging to the phytoestrogens, and its effects on the production of late-laying hens deserve more attention. This study evaluated the effects of dietary peppermint extract (PE) supplementation on egg production and quality, yolk fatty acid composition, antioxidant capacity, and cecal microbiota in late-phase laying hens.

Method

PE powder was identified by UPLC-MS/MS analysis. Two hundred and sixteen laying hens (60 weeks old) were randomly assigned to four treatments, each for 28 days: (i) basal diet (control group, CON); (ii) basal diet + 0.1% PE; (iii) basal diet + 0.2% PE; and (iv) basal diet + 0.4% PE. Egg, serum, and cecal samples were collected for analysis.

Results

Dietary PE supplementation increased the laying rate, serum triglyceride, immunoglobulin G, and total antioxidant capacity, while 0.2 and 0.4% PE supplementation increased eggshell thickness, serum total protein level, and superoxide dismutase activity of laying hens compared with the CON group (P < 0.05). PE addition in diets increased the C14:0, C18:3n3, C18:3n6, C23:0, C24:0, and C24:1n9 contents in the yolk. In addition, the egg yolk saturated fatty acid content was higher (P < 0.05) in the 0.2 and 0.4% PE groups compared with the CON and 0.1% PE groups. The microbiota analysis revealed that the cecal phylum Proteobacteria was decreased (P < 0.05) in the PE-supplemented groups. A total of 0.4% PE supplementation increased the cecal richness of gram-positive bacteria and decreased the richness of gram-negative and potentially pathogenic bacteria compared with the 0.1% PE group (P < 0.05). Microbial function prediction analysis showed that the cecal microbiota of the PE group was mainly enriched by fatty acid degradation, fatty acid metabolism, amino sugar metabolism, nucleotide sugar metabolism, and other pathways. Regression analysis suggested that 0.28-0.36% PE supplementation was the optimal level for improving egg production and quality, antioxidant capacity, and yolk fatty acid in late-phase laying hens.

Discussion

Dietary PE supplementation improved egg production and quality (including yolk fatty acid composition) by increasing serum IgG and antioxidant capacity and modulating the intestinal microbiota in late-phase laying hens.

Shotgun Metagenomic Sequencing Revealed the Prebiotic Potential of a Fruit Juice Drink with Fermentable Fibres in Healthy Humans

Fibre-based dietary interventions are at the forefront of gut microbiome modulation research, with a wealth of 16S rRNA information to demonstrate the prebiotic effects of isolated fibres. However, there is a distinct lack of data relating to the effect of a combination of soluble and insoluble fibres in a convenient-to-consume fruit juice food matrix on gut microbiota structure, diversity, and function. Here, we aimed to determine the impact of the MOJU Prebiotic Shot, an apple, lemon, ginger, and raspberry fruit juice drink blend containing chicory inulin, baobab, golden kiwi, and green banana powders, on gut microbiota structure and function. Healthy adults (n = 20) were included in a randomised, double-blind, placebo-controlled, cross-over study, receiving 60 mL MOJU Prebiotic Shot or placebo (without the fibre mix) for 3 weeks with a 3-week washout period between interventions. Shotgun metagenomics revealed significant between-group differences in alpha and beta diversity. In addition, the relative abundance of the phyla Actinobacteria and Desulfobacteria was significantly increased as a result of the prebiotic intervention. Nine species were observed to be differentially abundant (uncorrected p-value of <0.05) as a result of the prebiotic treatment. Of these, Bifidobacterium adolescentis and CAG-81 sp900066785 (Lachnospiraceae) were present at increased abundance relative to baseline. Additionally, KEGG analysis showed an increased abundance in pathways associated with arginine biosynthesis and phenylacetate degradation during the prebiotic treatment. Our results show the effects of the daily consumption of 60 mL MOJU Prebiotic Shot for 3 weeks and provide insight into the functional potential of B. adolescentis.

Dissecting the impact of dietary fiber type on atherosclerosis in mice colonized with different gut microbial communities

Dietary fiber consumption has been linked with improved cardiometabolic health, however, human studies have reported large interindividual variations in the observed benefits. We tested whether the effects of dietary fiber on atherosclerosis are influenced by the gut microbiome. We colonized germ-free ApoE-/- mice with fecal samples from three human donors (DonA, DonB, and DonC) and fed them diets supplemented with either a mix of 5 fermentable fibers (FF) or non-fermentable cellulose control (CC) diet. We found that DonA-colonized mice had reduced atherosclerosis burden with FF feeding compared to their CC-fed counterparts, whereas the type of fiber did not affect atherosclerosis in mice colonized with microbiota from the other donors. Microbial shifts associated with FF feeding in DonA mice were characterized by higher relative abundances of butyrate-producing taxa, higher butyrate levels, and enrichment of genes involved in synthesis of B vitamins. Our results suggest that atheroprotection in response to FF is not universal and is influenced by the gut microbiome.

Methods in Nutrition & Gut Microbiome Research: An American Society for Nutrition Satellite Session [13 October 2022]

The microbial cells colonizing the human body form an ecosystem that is integral to the regulation and maintenance of human health. Elucidation of specific associations between the human microbiome and health outcomes is facilitating the development of microbiome-targeted recommendations and treatments (e.g., fecal microbiota transplant; pre-, pro-, and post-biotics) to help prevent and treat disease. However, the potential of such recommendations and treatments to improve human health has yet to be fully realized. Technological advances have led to the development and proliferation of a wide range of tools and methods to collect, store, sequence, and analyze microbiome samples. However, differences in methodology at each step in these analytic processes can lead to variability in results due to the unique biases and limitations of each component. This technical variability hampers the detection and validation of associations with small to medium effect sizes. Therefore, the American Society for Nutrition (ASN) Nutritional Microbiology Group Engaging Members (GEM), sponsored by the Institute for the Advancement of Food and Nutrition Sciences (IAFNS), hosted a satellite session on methods in nutrition and gut microbiome research to review currently available methods for microbiome research, best practices, as well as tools and standards to aid in comparability of methods and results. This manuscript summarizes the topics and research discussed at the session. Consideration of the guidelines and principles reviewed in this session will increase the accuracy, precision, and comparability of microbiome research and ultimately the understanding of the associations between the human microbiome and health.

PyAGH: a python package to fast construct kinship matrices based on different levels of omic data

BACKGROUND

Construction of kinship matrices among individuals is an important step for both association studies and prediction studies based on different levels of omic data. Methods for constructing kinship matrices are becoming diverse and different methods have their specific appropriate scenes. However, software that can comprehensively calculate kinship matrices for a variety of scenarios is still in an urgent demand.

RESULTS

In this study, we developed an efficient and user-friendly python module, PyAGH, that can accomplish (1) conventional additive kinship matrces construction based on pedigree, genotypes, abundance data from transcriptome or microbiome; (2) genomic kinship matrices construction in combined population; (3) dominant and epistatic effects kinship matrices construction; (4) pedigree selection, tracing, detection and visualization; (5) visualization of cluster, heatmap and PCA analysis based on kinship matrices. The output from PyAGH can be easily integrated in other mainstream software based on users' purposes. Compared with other softwares, PyAGH integrates multiple methods for calculating the kinship matrix and has advantages in terms of speed and data size compared to other software. PyAGH is developed in python and C +  + and can be easily installed by pip tool. Installation instructions and a manual document can be freely available from https://github.com/zhaow-01/PyAGH .

CONCLUSION

PyAGH is a fast and user-friendly Python package for calculating kinship matrices using pedigree, genotype, microbiome and transcriptome data as well as processing, analyzing and visualizing data and results. This package makes it easier to perform predictions and association studies processes based on different levels of omic data.

Predicting metabolic response to dietary intervention using deep learning

Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolic responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living in our gastrointestinal tract, is highly personalized and plays a key role in our metabolic responses to foods and nutrients. Accurately predicting metabolic responses to dietary interventions based on individuals' gut microbial compositions holds great promise for precision nutrition. Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. Here we develop a new method McMLP (Metabolic response predictor using coupled Multilayer Perceptrons) to fill in this gap. We provide clear evidence that McMLP outperforms existing methods on both synthetic data generated by the microbial consumer-resource model and real data obtained from six dietary intervention studies. Furthermore, we perform sensitivity analysis of McMLP to infer the tripartite food-microbe-metabolite interactions, which are then validated using the ground-truth (or literature evidence) for synthetic (or real) data, respectively. The presented tool has the potential to inform the design of microbiota-based personalized dietary strategies to achieve precision nutrition.

Probiotics and Postbiotics as the Functional Food Components Affecting the Immune Response

The food market is one of the most innovative segments of the world economy. Recently, among consumers there is a forming trend of a healthier lifestyle and interest in functional foods. Products with positive health properties are a good source of nutrients for consumers' nutritional needs and reduce the risk of metabolic diseases such as diabetes, atherosclerosis, or obesity. They also seem to boost the immune system. One of the types of functional food is "probiotic products", which contain viable microorganisms with beneficial health properties. However, due to some technical difficulties in their development and marketing, a new alternative has started to be sought. Many scientific studies also point to the possibility of positive effects on human health, the so-called "postbiotics", the characteristic metabolites of the microbiome. Both immunobiotics and post-immunobiotics are the food components that affect the immune response in two ways: as inhibition (suppressing allergies and inflammation) or as an enhancement (providing host defenses against infection). This work's aim was to conduct a literature review of the possibilities of using probiotics and postbiotics as the functional food components affecting the immune response, with an emphasis on the most recently published works.

Gut microbiota modulates differential lipid metabolism outcomes associated with FTO gene polymorphisms in response to personalized nutrition intervention

Background

Interindividual differences in response to personalized nutrition (PN) intervention were affected by multiple factors, including genetic backgrounds and gut microbiota. The fat mass and obesity associated (FTO) gene is an important factor related to hyperlipidemia and occurrence of cardiovascular diseases. However, few studies have explored the differences in response to intervention among subjects with different genotypes of FTO, and the associations between gut microbiota and individual responses.

Objective

To explore the differential lipid metabolism outcomes associated with FTO gene polymorphisms in response to PN intervention, the altered taxonomic features of gut microbiota caused by the intervention, and the associations between gut microbiota and lipid metabolism outcomes.

Methods

A total of 400 overweight or obese adults were recruited in the study and randomly divided into the PN group and control group, of whom 318 completed the 12-week intervention. The single nucleotide polymorphism (SNP) of rs1121980 in FTO was genotyped. Gut microbiota and blood lipids were determined at baseline and week 12. Functional property of microbiota was predicted using Tax4Fun functional prediction analysis.

Results

Subjects with the risk genotype of FTO had significantly higher weight and waist circumference (WC) at baseline. Generalized linear regression models showed that the reduction in weight, body mass index (BMI), WC, body fat percentage, total cholesterol (TCHO), and low-density lipoprotein (LDL) was greater in subjects with the risk genotype of FTO and in the PN group. Significant interaction effects between genotype and intervention on weight, BMI, WC, TCHO, and LDL were found after stratifying for specific genotype of FTO. All subjects showed significant increasement in α diversity of gut microbiota after intervention except for those with the non-risk genotype in the control group. Gut microbiota, including Blautia and Firmicutes, might be involved in lipid metabolism in response to interventions. The predicted functions of the microbiota in subjects with different genotypes were related to lipid metabolism-related pathways, including fatty acid biosynthesis and degradation.

Conclusion

Subjects with the risk genotype of FTO had better response to nutrition intervention, and PN intervention showed better amelioration in anthropometric parameters and blood lipids than the control. Gut microbiota might be involved in modulating differential lipid metabolism responses to intervention in subjects with different genotypes.

Trial registration

[Chictr.org.cn], identifier [ChiCTR1900026226].

Diet-microbiome interactions in cancer treatment: Opportunities and challenges for precision nutrition in cancer

Dietary patterns contribute to cancer risk. Separately, microbial factors influence the development of several cancers. However, the interaction of diet and the microbiome and their joint contribution to cancer treatment response needs more research. The microbiome significantly impacts drug metabolism, immune activation, and response to immunotherapy. One of the critical factors affecting the microbiome structure and function is diet. Data demonstrate that the diet and microbiome composition affects the immune response. Moreover, malnutrition is a significant confounder to cancer therapy response. There is little understanding of the interaction of malnutrition with the microbiome in the context of cancer. This review aims to address the current knowledge of dietary intake patterns and malnutrition among cancer patients and the impact on treatment outcomes. Second, this review will provide evidence linking the microbiome to cancer treatment response and provide evidence of the potentially strong effect that diet could have on this interaction. This review will formulate critical questions that will need further research to understand the diet-microbiome relationship in cancer treatment response and directions for future research to guide us to precision nutrition therapy to improve cancer outcomes.

Impact of a 7-day homogeneous diet on interpersonal variation in human gut microbiomes and metabolomes

Gut microbiota metabolism of dietary compounds generates a vast array of microbiome-dependent metabolites (MDMs), which are highly variable between individuals. The uremic MDMs (uMDMs) phenylacetylglutamine (PAG), p-cresol sulfate (PCS), and indoxyl sulfate (IS) accumulate during renal failure and are associated with poor outcomes. Targeted dietary interventions may reduce toxic MDM generation; however, it is unclear if inter-individual differences in diet or gut microbiome dominantly contribute to MDM variance. Here, we use a 7-day homogeneous average American diet to standardize dietary precursor availability in 21 healthy individuals. During dietary homogeneity, the coefficient of variation in PAG, PCS, and IS (primary outcome) did not decrease, nor did inter-individual variation in most identified metabolites; other microbiome metrics showed no or modest responses to the intervention. Host identity and age are dominant contributors to variability in MDMs. These results highlight the potential need to pair dietary modification with microbial therapies to control MDM profiles.

Gut microbiota predicts body fat change following a low-energy diet: a PREVIEW intervention study

Background

Low-energy diets (LEDs) comprise commercially formulated food products that provide between 800 and 1200 kcal/day (3.3–5 MJ/day) to aid body weight loss. Recent small-scale studies suggest that LEDs are associated with marked changes in the gut microbiota that may modify the effect of the LED on host metabolism and weight loss. We investigated how the gut microbiota changed during 8 weeks of total meal replacement LED and determined their associations with host response in a sub-analysis of 211 overweight adults with pre-diabetes participating in the large multicentre PREVIEW (PREVention of diabetes through lifestyle intervention and population studies In Europe and around the World) clinical trial.

Methods

Microbial community composition was analysed by Illumina sequencing of the hypervariable V3-V4 regions of the 16S ribosomal RNA (rRNA) gene. Butyrate production capacity was estimated by qPCR targeting the butyryl-CoA:acetate CoA-transferase gene. Bioinformatics and statistical analyses, such as comparison of alpha and beta diversity measures, correlative and differential abundances analysis, were undertaken on the 16S rRNA gene sequences of 211 paired (pre- and post-LED) samples as well as their integration with the clinical, biomedical and dietary datasets for predictive modelling.

Results

The overall composition of the gut microbiota changed markedly and consistently from pre- to post-LED (P = 0.001), along with increased richness and diversity (both P < 0.001). Following the intervention, the relative abundance of several genera previously associated with metabolic improvements (e.g., Akkermansia and Christensenellaceae R-7 group) was significantly increased (P < 0.001), while flagellated Pseudobutyrivibrio, acetogenic Blautia and Bifidobacterium spp. were decreased (all P < 0.001). Butyrate production capacity was reduced (P < 0.001). The changes in microbiota composition and predicted functions were significantly associated with body weight loss (P < 0.05). Baseline gut microbiota features were able to explain ~25% of variation in total body fat change (post–pre-LED).

Conclusions

The gut microbiota and individual taxa were significantly influenced by the LED intervention and correlated with changes in total body fat and body weight in individuals with overweight and pre-diabetes. Despite inter-individual variation, the baseline gut microbiota was a strong predictor of total body fat change during the energy restriction period.

Trial registration

The PREVIEW trial was prospectively registered at ClinicalTrials.gov (NCT01777893) on January 29, 2013.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01053-7.

Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease

Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and the main leading cause of morbidity and mortality worldwide, posing a huge socio-economic burden to the society and health systems. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current CAD risk prediction approaches are based on a small number of traditional risk factors (age, sex, diabetes, LDL and HDL cholesterol, smoking, systolic blood pressure) and are incompletely predictive across all patient groups, as CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors. Diet is one of the modifiable factors for improving lifestyle and disease prevention. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD indicates that the “one-size-fits-all” approach may not be efficient, due to significant variation in inter-individual responses. Recently, the gut microbiome has emerged as a potential and previously under-explored contributor to these variations. Hence, efficient integration of dietary and gut microbiome information alongside with genetic variations and clinical data holds a great promise to improve CAD risk prediction. Nevertheless, the highly complex nature of meals combined with the huge inter-individual variability of the gut microbiome poses several Big Data analytics challenges in modeling diet-gut microbiota interactions and integrating these within CAD risk prediction approaches for the development of personalized decision support systems (DSS). In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives, as these approaches are able to capture large and complex matrices of data, incorporating their interactions and identifying both linear and non-linear relationships. In this Mini-Review, we consider (1) the most used AI/ML approaches and their different use cases for CAD risk prediction (2) modeling of the content, choice and impact of dietary factors on CAD risk; (3) classification of individuals by their gut microbiome composition into CAD cases vs. controls and (4) modeling of the diet-gut microbiome interactions and their impact on CAD risk. Finally, we provide an outlook for putting it all together for improved CAD risk predictions.

Predicting the recurrence of chronic rhinosinusitis with nasal polyps using nasal microbiota

BACKGROUND

Recent studies have revealed that the nasal microbiota in patients with chronic rhinosinusitis with nasal polyps (CRSwNP) is profoundly altered and is correlated with systemic inflammation. However, little is known regarding whether the microbiota can be utilized to predict nasal polyp recurrence. This study is aimed to determine whether altered nasal microbiota constituents could be used as biomarkers to predict CRSwNP recurrence.

METHODS

Nasal microbiota constituents were quantified and characterized using bacterial 16S ribosomal RNA gene sequencing. Selected features for least absolute shrinkage and selection operator regression-based predictors were the nasal microbiota community composition and CRSwNP patient clinical characteristics. The primary outcome was recurrence, which was determined post-admission.

RESULTS

By distinguishing recurrence-associated nasal microbiota taxa and exploiting the distinct nasal microbiota abundance between patients with recurrent and non-recurrent CRSwNP, we developed a predictive classifier for the diagnosis of nasal polyps' recurrence with 91.4% accuracy.

CONCLUSIONS

Key taxonomical features of the nasal microbiome could predict recurrence in CRSwNP patients. The nasal microbiome is an understudied source of clinical variation in CRSwNP and represents a novel therapeutic target for future prevention and treatment.

Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods

The diet plays a major role in shaping gut microbiome composition and function in both humans and animals, and dietary intervention trials are often used to investigate and understand these effects. A plethora of statistical methods for analysing the differential abundance of microbial taxa exists, and new methods are constantly being developed, but there is a lack of benchmarking studies and clear consensus on the best multivariate statistical practices. This makes it hard for a biologist to decide which method to use. We compared the outcomes of generic multivariate ANOVA (ASCA and FFMANOVA) against statistical methods commonly used for community analyses (PERMANOVA and SIMPER) and methods designed for analysis of count data from high-throughput sequencing experiments (ALDEx2, ANCOM and DESeq2). The comparison is based on both simulated data and five published dietary intervention trials representing different subjects and study designs. We found that the methods testing differences at the community level were in agreement regarding both effect size and statistical significance. However, the methods that provided ranking and identification of differentially abundant operational taxonomic units (OTUs) gave incongruent results, implying that the choice of method is likely to influence the biological interpretations. The generic multivariate ANOVA tools have the flexibility needed for analysing multifactorial experiments and provide outputs at both the community and OTU levels; good performance in the simulation studies suggests that these statistical tools are also suitable for microbiome data sets.

Tripartite relationship between gut microbiota, intestinal mucus and dietary fibers: towards preventive strategies against enteric infections

The human gut is inhabited by a large variety of microorganims involved in many physiological processes and collectively referred as to gut microbiota. Disrupted microbiome has been associated with negative health outcomes and especially could promote the onset of enteric infections. To sustain their growth and persistence within the human digestive tract, gut microbes and enteric pathogens rely on two main polysaccharide compartments, namely dietary fibers and mucus carbohydrates. Several evidences suggest that the three-way relationship between gut microbiota, dietary fibers and mucus layer could unravel the capacity of enteric pathogens to colonise the human digestive tract and ultimately lead to infection. The review starts by shedding light on similarities and differences between dietary fibers and mucus carbohydrates structures and functions. Next, we provide an overview of the interactions of these two components with the third partner, namely, the gut microbiota, under health and disease situations. The review will then provide insights into the relevance of using dietary fibers interventions to prevent enteric infections with a focus on gut microbial imbalance and impaired-mucus integrity. Facing the numerous challenges in studying microbiota-pathogen-dietary fiber-mucus interactions, we lastly describe the characteristics and potentialities of currently available in vitro models of the human gut.

Diet and the Microbiota-Gut-Brain Axis: Sowing the Seeds of Good Mental Health

Over the past decade, the gut microbiota has emerged as a key component in regulating brain processes and behavior. Diet is one of the major factors involved in shaping the gut microbiota composition across the lifespan. However, whether and how diet can affect the brain via its effects on the microbiota is only now beginning to receive attention. Several mechanisms for gut-to-brain communication have been identified, including microbial metabolites, immune, neuronal, and metabolic pathways, some of which could be prone to dietary modulation. Animal studies investigating the potential of nutritional interventions on the microbiota-gut-brain axis have led to advancements in our understanding of the role of diet in this bidirectional communication. In this review, we summarize the current state of the literature triangulating diet, microbiota, and host behavior/brain processes and discuss potential underlying mechanisms. Additionally, determinants of the responsiveness to a dietary intervention and evidence for the microbiota as an underlying modulator of the effect of diet on brain health are outlined. In particular, we emphasize the understudied use of whole-dietary approaches in this endeavor and the need for greater evidence from clinical populations. While promising results are reported, additional data, specifically from clinical cohorts, are required to provide evidence-based recommendations for the development of microbiota-targeted, whole-dietary strategies to improve brain and mental health.

Effect of Freezing on Gut Microbiota Composition and Functionality for In Vitro Fermentation Experiments

The gut microbiota has a profound effect on human health and is modulated by food and bioactive compounds. To study such interaction, in vitro batch fermentations are performed with fecal material, and some experimental designs may require that such fermentations be performed with previously frozen stools. Although it is known that freezing fecal material does not alter the composition of the microbial community in 16S rRNA gene amplicon and metagenomic sequencing studies, it is not known whether the microbial community in frozen samples could still be used for in vitro fermentations. To explore this, we undertook a pilot study in which in vitro fermentations were performed with fecal material from celiac, cow’s milk allergic, obese, or lean children that was frozen (or not) with 20% glycerol. Before fermentation, the fecal material was incubated in a nutritious medium for 6 days, with the aim of giving the microbial community time to recover from the effects of freezing. An aliquot was taken daily from the stabilization vessel and used for the in vitro batch fermentation of lentils. The microbial community structure was significantly different between fresh and frozen samples, but the variation introduced by freezing a sample was always smaller than the variation among individuals, both before and after fermentation. Moreover, the potential functionality (as determined in silico by a genome-scaled metabolic reconstruction) did not differ significantly, possibly due to functional redundancy. The most affected genus was Bacteroides, a fiber degrader. In conclusion, if frozen fecal material is to be used for in vitro fermentation purposes, our preliminary analyses indicate that the functionality of microbial communities can be preserved after stabilization.

Dietary Moutan Cortex Radicis Improves Serum Antioxidant Capacity and Intestinal Immunity and Alters Colonic Microbiota in Weaned Piglets

Background:Moutan cortex radicis (MCR), as a common traditional Chinese medicine, has been widely used as an antipyretic, antiseptic, and anti-inflammatory agent in China.

Objectives: This study aimed to investigate the effects of dietary MCR supplementation on the antioxidant capacity and intestinal health of the pigs and to explore whether MCR exerts positive effects on intestinal health via regulating nuclear factor kappa-B (NF-κB) signaling pathway and intestinal microbiota.

Methods: MCR powder was identified by LC-MS analysis. Selected 32 weaned piglets (21 d of age, 6.37 ± 0.10 kg average BW) were assigned (8 pens/diet, 1 pig/pen) to 4 groups and fed with a corn-soybean basal diet supplemented with 0, 2,000, 4,000, and 8,000 mg/kg MCR for 21 d. After the piglets were sacrificed, antioxidant indices, histomorphology examination, and inflammatory signaling pathway expression were assessed. The 16s RNA sequencing was used to analyze the effects of MCR on the intestinal microbiota structure of piglets.

Results: Supplemental 4,000 mg/kg MCR significantly increased (P < 0.05) the average daily weight gain (ADG), average daily feed intake (ADFI), total antioxidative capability, colonic short-chain fatty acids (SCFA) concentrations, and the crypt depth in the jejunum but decreased (P < 0.05) the mRNA expression levels of interferon γ, tumor necrosis factor-α, interleukin-1β, inhibiting kappa-B kinase β (IKKβ), inhibiting nuclear factor kappa-B (IκBα), and NF-κB in the jejunum and ileum. Microbiota sequencing identified that MCR supplementation significantly increased the microbial richness indices (Chao1, ACE, and observed species, P < 0.05) and the relative abundances of Firmicutes and Lactobacillus (P < 0.05), decreased the relative abundances of Bacteroides, Parabacteroides, unidentified_Lachnospiraceae, and Enterococcus (P < 0.05) and had no significant effects on the diversity indices (Shannon and Simpson, P > 0.05). Microbial metabolic phenotypes analysis also showed that the richness of aerobic bacteria and facultative anaerobic bacteria, oxidative stress tolerance, and biofilm forming were significantly increased (P < 0.05), and the richness of anaerobic bacteria and pathogenic potential of gut microbiota were reduced (P < 0.05) by MCR treatment. Regression analysis showed that the optimal MCR supplemental level for growth performance, serum antioxidant capacity, and intestinal health of weaned piglets was 3,420 ~ 4,237 mg/kg.

Conclusions: MCR supplementation improved growth performance and serum antioxidant capacity, and alleviated intestinal inflammation by inhibiting the IKKβ/IκBα/NF-κB signaling pathway and affecting intestinal microbiota in weaned piglets.

Dietary fiber and the microbiota: A narrative review by a group of experts from the Asociación Mexicana de Gastroenterología

Dietary fiber intake is one of the most influential and efficacious strategies for modulating the gut microbiota. Said fiber can be digested by the microbiota itself, producing numerous metabolites, which include the short-chain fatty acids (SCFAs). SCFAs have local and systemic functions that impact the composition and function of the gut microbiota, and consequently, human health. The aim of the present narrative review was to provide a document that serves as a frame of reference for a clear understanding of dietary fiber and its direct and indirect effects on health. The direct benefits of dietary fiber intake can be dependent on or independent of the gut microbiota. The use of dietary fiber by the gut microbiota involves several factors, including the fiber's physiochemical characteristics. Dietary fiber type influences the gut microbiota because not all bacterial species have the same capacity to produce the enzymes needed for its degradation. A low-fiber diet can affect the balance of the SCFAs produced. Dietary fiber indirectly benefits cardiometabolic health, digestive health, certain functional gastrointestinal disorders, and different diseases.

Positive influence of gut microbiota on the effects of Korean red ginseng in metabolic syndrome: a randomized, double-blind, placebo-controlled clinical trial

Background

Ginseng, a traditional herbal medicine, has been used for thousands of years to treat various diseases including metabolic syndrome (MS). However, the underlying mechanism(s) of such beneficial actions of ginseng against MS is poorly understood. Emerging evidence indicates a close association of the host gut microbiota with MS. The present study was conducted to examine, whether the beneficial effects of Korean red ginseng (KRG) against MS could be influenced by gut microbial population and whether gut microbial profile could be considered a valuable biomarker for targeted treatment strategy for MS in compliance with the predictive, preventive, and personalized medicine (PPPM / 3PM).

Methods

This clinical study was a randomized, double-blind, placebo-controlled trial evaluating the effects of KRG treatment for 8 weeks on patients with MS. The anthropometric parameters, vital signs, metabolic biomarkers, and gut microbial composition through 16S rRNA gene sequencing were assessed at the baseline and endpoint. The impact of KRG was also evaluated after categorizing the subjects into responders and non-responders, as well as enterotypes 1 and 2 based on their gut microbial profile at the baseline.

Results

Fifty out of 60 subjects who meet the MS criteria completed the trial without showing adverse reactions. The KRG treatment caused a significant decrease in systolic blood pressure (SBP). Microbial analysis revealed a decrease in Firmicutes, Proteobacteria, and an increase in Bacteroidetes in response to KRG. In patient stratification analysis, the responders showing marked improvement in the serum levels of lipid metabolic biomarkers TC and LDL due to the KRG treatment exhibited higher population of both the family Lachnospiraceae and order Clostridiales compared to the non-responders. The homeostasis model assessment-insulin resistance (HOMA-IR) and insulin level were decreased in enterotype 1 (Bacteroides-abundant group) and increased in enterotype 2 (prevotella-abundant group) following the KRG treatment.

Conclusion

In this study, the effects of KRG on the glucose metabolism in MS patients were influenced by the relative abundances of gut microbial population and differed according to the individual enterotype. Therefore, the analysis of enterotype categories is considered to be helpful in predicting the effectiveness of KRG on glucose homeostasis of MS patients individually. This will further help to decide on the appropriate treatment strategy for MS, in compliance with the perspective of PPPM.

Dietary fiber and the microbiota: A narrative review by a group of experts from the Asociación Mexicana de Gastroenterología

Dietary fiber intake is one of the most influential and efficacious strategies for modulating the gut microbiota. Said fiber can be digested by the microbiota itself, producing numerous metabolites, which include the short-chain fatty acids (SCFAs). SCFAs have local and systemic functions that impact the composition and function of the gut microbiota, and consequently, human health. The aim of the present narrative review was to provide a document that serves as a frame of reference for a clear understanding of dietary fiber and its direct and indirect effects on health. The direct benefits of dietary fiber intake can be dependent on or independent of the gut microbiota. The use of dietary fiber by the gut microbiota involves several factors, including the fiber's physiochemical characteristics. Dietary fiber type influences the gut microbiota because not all bacterial species have the same capacity to produce the enzymes needed for its degradation. A low-fiber diet can affect the balance of the SCFAs produced. Dietary fiber indirectly benefits cardiometabolic health, digestive health, certain functional gastrointestinal disorders, and different diseases.

Assessing mediating effects of high-dimensional microbiome measurements in dietary intervention studies

Habitual diet can influence health-related outcomes directly, but such effects may also be modulated indirectly by gut microbiota. We consider randomized trials and the question to what extent the effect of diet on an outcome of interest is mediated through the gut microbiome or whether there is a diet-microbiome interaction identifying subgroups of individuals who are more susceptible to specific dietary effects. The baseline microbiome by itself may be a modifier of the effects of diet on health. Yet, the high dimensionality of microbiome data requires innovative statistical approaches to identify potential mediating or moderating effects. To motivate our proposal for an appropriate analysis workflow, we consider a randomized trial that investigates the effect of a 4-week vegan diet on the diversity of gut microbiota and branched-chain amino acid metabolism in healthy omnivorous volunteers. To address the challenge of compositional microbiome data, we consider an adaptation of the lasso for penalized estimation of multivariable regression models with a large number of microbiotic taxa. This is plugged into a classical regression mediation effect analysis strategy. The interaction effects are obtained via an approach that can directly estimate them without having to deal with main effects. As a result we obtain signatures comprised of microbiotic taxa with potential mediating and moderating effects. Some taxa no longer show up as mediating, when taking moderating effects into account. Thus, the proposed analysis strategy allows to identify specific mediating effects, while avoiding potential erroneous conclusions, where moderating effects might have believed to be mediating effects.

An Overview of Current Knowledge of the Gut Microbiota and Low-Calorie Sweeteners

This review provides an overview of the interrelationships among the diet, gut microbiota and health status, and then focuses specifically on published research assessing the relationship of low/no-calorie sweeteners (LNCS) to selected aspects of the gut microbiota. Microbiome research is expanding as new data on its role in health and disease vulnerability emerge. The gut microbiome affects health, digestion, and susceptibility to disease. In the last 10 years, investigations of LNCS effects on the gut microbiota have proliferated, though results are conflicting and are often confounded by differences in study design such as study diet, the form of the test article, dosage, and study population. Staying current on microbiome research and the role of dietary inputs, like LNCS, will allow healthcare and nutrition practitioners to provide evidenced-based guidance to the individuals they serve.

Causal Inference in Microbiome Medicine: Principles and Applications

Microorganisms that colonize the mammalian skin and cavity play critical roles in various physiological functions of the host. Numerous studies have revealed strong associations between the microbiota and multiple diseases. However, association does not mean causation. To clarify the mechanisms underlying microbiota-mediated diseases, research is moving from associative analyses to causation studies. In this article, we first introduce the principles of the computational methods for causal inference, and then discuss the applications of these methods in microbiome medicine. Furthermore, we examine the reliability of theoretically inferred causality by the interventionist framework. Finally, we show the potential of confirmed causality in microbiota-targeted therapy, especially in personalized dietary intervention. We conclude that a comprehensive understanding of the causal relationships between diets, microbiota, host targets, and diseases is critical to future microbiome medicine.

Best Practices for Microbiome Study Design in Companion Animal Research

The gut microbiome is a community of microorganisms that inhabits an animal host's gastrointestinal tract, with important effects on animal health that are shaped by multiple environmental, dietary, and host-associated factors. Clinical and dietary trials in companion animals are increasingly including assessment of the microbiome, but interpretation of these results is often hampered by suboptimal choices in study design. Here, we review best practices for conducting feeding trials or clinical trials that intend to study the effects of an intervention on the microbiota. Choices for experimental design, including a review of basic designs, controls, and comparison groups, are discussed in the context of special considerations necessary for microbiome studies. Diet is one of the strongest influences on the composition of gut microbiota, so applications specific to nutritional interventions are discussed in detail. Lastly, we provide specific advice for successful recruitment of colony animals and household pets into an intervention study. This review is intended to serve as a resource to academic and industry researchers, clinicians, and veterinarians alike, for studies that test many different types of interventions.

Resistant Starch Type 2 from Wheat Reduces Postprandial Glycemic Response with Concurrent Alterations in Gut Microbiota Composition

The majority of research on the physiological effects of dietary resistant starch type 2 (RS2) has focused on sources derived from high-amylose maize. In this study, we conduct a double-blind, randomized, placebo-controlled, crossover trial investigating the effects of RS2 from wheat on glycemic response, an important indicator of metabolic health, and the gut microbiota. Overall, consumption of RS2-enriched wheat rolls for one week resulted in reduced postprandial glucose and insulin responses relative to conventional wheat when participants were provided with a standard breakfast meal containing the respective treatment rolls (RS2-enriched or conventional wheat). This was accompanied by an increase in the proportions of bacterial taxa Ruminococcus and Gemmiger in the fecal contents, reflecting the composition in the distal intestine. Additionally, fasting breath hydrogen and methane were increased during RS2-enriched wheat consumption. However, although changes in fecal short-chain fatty acid (SCFA) concentrations were not significant between control and RS-enriched wheat roll consumption, butyrate and total SCFAs were positively correlated with relative abundance of Faecalibacterium, Ruminoccocus, Roseburia, and Barnesiellaceae. These effects show that RS2-enriched wheat consumption results in a reduction in postprandial glycemia, altered gut microbial composition, and increased fermentation activity relative to wild-type wheat.

The complexities of the diet-microbiome relationship: advances and perspectives

Personalised dietary modulation of the gut microbiota may be key to disease management. Current investigations provide a broad understanding of the impact of diet on the composition and activity of the gut microbiota, yet detailed knowledge in applying diet as an actionable tool remains limited. Further to the relative novelty of the field, approaches are yet to be standardised and extremely heterogeneous research outcomes have ensued. This may be related to confounders associated with complexities in capturing an accurate representation of both diet and the gut microbiota. This review discusses the intricacies and current methodologies of diet-microbial relations, the implications and limitations of these investigative approaches, and future considerations that may assist in accelerating applications. New investigations should consider improved collection of dietary data, further characterisation of mechanistic interactions, and an increased focus on -omic technologies such as metabolomics to describe the bacterial and metabolic activity of food degradation, together with its crosstalk with the host. Furthermore, clinical evidence with health outcomes is required before therapeutic dietary strategies for microbial amelioration can be made. The potential to reach detailed understanding of diet-microbiota relations may depend on re-evaluation, progression, and unification of research methodologies, which consider the complexities of these interactions.

Comparative dietary sulfated metabolome analysis reveals unknown metabolic interactions of the gut microbiome and the human host

The gut microbiome converts dietary compounds that are absorbed in the gastrointestinal tract and further metabolized by the human host. Sulfated metabolites are a major compound class derived from this co-metabolism and have been linked to disease development. In the present multidisciplinary study, we have investigated human urine samples from a dietary intervention study with 22 individuals collected before and after consumption of a polyphenol rich breakfast. These samples were analyzed utilizing our method combining enzymatic metabolite hydrolysis using an arylsulfatase and mass spectrometric metabolomics. Key to this study is the validation of 235 structurally diverse sulfated metabolites. We have identified 48 significantly upregulated metabolites upon dietary intervention including 11 previously unknown sulfated metabolites for this diet. We observed a large variation in subjects based on their potential to sulfate metabolites, which may be the foundation for classification of subjects as high and low sulfate metabolizers in future large cohort studies. The reported sulfatase-based method is a robust tool for the discovery of unknown microbiota-derived metabolites in human samples.

Infant gut microbiota characteristics generally do not modify effects of lipid-based nutrient supplementation on growth or inflammation: secondary analysis of a randomized controlled trial in Malawi

An unhealthy gut microbial community may act as a barrier to improvement in growth and health outcomes in response to nutritional interventions. The objective of this analysis was to determine whether the infant microbiota modified the effects of a randomized controlled trial of lipid-based nutrient supplements (LNS) in Malawi on growth and inflammation at 12 and 18 months, respectively. We characterized baseline microbiota composition of fecal samples at 6 months of age (n = 506, prior to infant supplementation, which extended to 18 months) using 16S rRNA gene sequencing of the V4 region. Features of the gut microbiota previously identified as being involved in fatty acid or micronutrient metabolism or in outcomes relating to growth and inflammation, especially in children, were investigated. Prior to correction for multiple hypothesis testing, the effects of LNS on growth appeared to be modified by Clostridium (p-for-interaction = 0.02), Ruminococcus (p-for-interaction = 0.007), and Firmicutes (p-for-interaction = 0.04) and effects on inflammation appeared to be modified by Faecalibacterium (p-for-interaction = 0.03) and Streptococcus (p-for-interaction = 0.004). However, after correction for multiple hypothesis testing these findings were not statistically significant, suggesting that the gut microbiota did not alter the effect of LNS on infant growth and inflammation in this cohort.

Strain-level epidemiology of microbial communities and the human microbiome

The biological importance and varied metabolic capabilities of specific microbial strains have long been established in the scientific community. Strains have, in the past, been largely defined and characterized based on microbial isolates. However, the emergence of new technologies and techniques has enabled assessments of their ecology and phenotypes within microbial communities and the human microbiome. While it is now more obvious how pathogenic strain variants are detrimental to human health, the consequences of subtle genetic variation in the microbiome have only recently been exposed. Here, we review the operational definitions of strains (e.g., genetic and structural variants) as they can now be identified from microbial communities using different high-throughput, often culture-independent techniques. We summarize the distribution and diversity of strains across the human body and their emerging links to health maintenance, disease risk and progression, and biochemical responses to perturbations, such as diet or drugs. We list methods for identifying, quantifying, and tracking strains, utilizing high-throughput sequencing along with other molecular and “culturomics” technologies. Finally, we discuss implications of population studies in bridging experimental gaps and leading to a better understanding of the health effects of strains in the human microbiome.

Gut microbiota of obese and diabetic Thai subjects and interplay with dietary habits and blood profiles

Obesity and type 2 diabetes mellitus (T2DM) have become major public health issues globally. Recent research indicates that intestinal microbiota play roles in metabolic disorders. Though there are numerous studies focusing on gut microbiota of health and obesity states, those are primarily focused on Western countries. Comparatively, only a few investigations exist on gut microbiota of people from Asian countries. In this study, the fecal microbiota of 30 adult volunteers living in Chiang Rai Province, Thailand were examined using next-generation sequencing (NGS) in association with blood profiles and dietary habits. Subjects were categorized by body mass index (BMI) and health status as follows; lean (L) = 8, overweight (OV) = 8, obese (OB) = 7 and diagnosed T2DM = 7. Members of T2DM group showed differences in dietary consumption and fasting glucose level compared to BMI groups. A low level of high-density cholesterol (HDL) was observed in the OB group. Principal coordinate analysis (PCoA) revealed that microbial communities of T2DM subjects were clearly distinct from those of OB. An analogous pattern was additionally illustrated by multiple factor analysis (MFA) based on dietary habits, blood profiles, and fecal gut microbiota in BMI and T2DM groups. In all four groups, Bacteroidetes and Firmicutes were the predominant phyla. Abundance of Faecalibacterium prausnitzii, a butyrate-producing bacterium, was significantly higher in OB than that in other groups. This study is the first to examine the gut microbiota of adult Thais in association with dietary intake and blood profiles and will provide the platform for future investigations.

Age- and duration-dependent effects of whey protein on high-fat diet-induced changes in body weight, lipid metabolism, and gut microbiota in mice

Bovine whey protein has been demonstrated to exert a positive effect on energy balance, lipid metabolism, and nutrient absorption. Additionally, it affects gut microbiota configuration. Thus, whey protein is considered as good dietary candidate to prevent or ameliorate metabolic diseases, such as obesity. However, the relationship that links energy balance, metabolism, and intestinal microbial population mediated by whey protein intake remains poorly understood. In this study, we investigated the beneficial effects attributed to whey protein in the context of high-fat diet (HFD) in mice at two different ages, with short or longer durations of whey protein supplementation. Here, a 5-week dietary intervention with HFD in combination with either whey protein isolate (WPI) or the control nonwhey milk protein casein (CAS) was performed using 5-week or 10-week-old C57BL/6J mice. Notably, the younger mice had no prior history of ingestion of WPI, while older mice did. 5-week-old HFD-WPI-fed mice showed a decrease in weight gain and changes in the expression of genes within the epidydimal white adipose tissue including those encoding leptin, inflammatory marker CD68, fasting-induced adipose factor FIAF and enzymes involved in fatty acids catabolism, relative to HFD-CAS-fed mice. Differences in β-diversity and higher proportions of Lactobacillus murinus, and related functions, were evident within the gut microbiota of HFD-WPI mice. However, none of these changes were observed in mice that started the HFD dietary intervention at 10-weeks-old, with an extended period of WPI supplementation. These results suggest that the effect of whey protein on mouse body weight, adipose tissue, and intestinal parameters depends on diet duration and stage of life during which the diet is provided. In some instances, WPI influences gut microbiota composition and functional potential, which might orchestrate observed metabolic and physiological modifications.

A Guide to Diet-Microbiome Study Design

Intense recent interest in understanding how the human gut microbiome influences health has kindled a concomitant interest in linking dietary choices to microbiome variation. Diet is known to be a driver of microbiome variation, and yet the precise mechanisms by which certain dietary components modulate the microbiome, and by which the microbiome produces byproducts and secondary metabolites from dietary components, are not well-understood. Interestingly, despite the influence of diet on the gut microbiome, the majority of microbiome studies published to date contain little or no analysis of dietary intake. Although an increasing number of microbiome studies are now collecting some form of dietary data or even performing diet interventions, there are no clear standards in the microbiome field for how to collect diet data or how to design a diet-microbiome study. In this article, we review the current practices in diet-microbiome analysis and study design and make several recommendations for best practices to provoke broader discussion in the field. We recommend that microbiome studies include multiple consecutive microbiome samples per study timepoint or phase and multiple days of dietary history prior to each microbiome sample whenever feasible. We find evidence that direct effects of diet on the microbiome are likely to be observable within days, while the length of an intervention required for observing microbiome-mediated effects on the host phenotype or host biomarkers, depending on the outcome, may be much longer, on the order of weeks or months. Finally, recent studies demonstrating that diet-microbiome interactions are personalized suggest that diet-microbiome studies should either include longitudinal sampling within individuals to identify personalized responses, or should include an adequate number of participants spanning a range of microbiome types to identify generalized responses.

Immunometabolism: new insights and lessons from antigen-directed cellular immune responses

Modulation of immune responses by nutrients is an important area of study in cellular biology and clinical sciences in the context of cancer therapies and anti-pathogen-directed immune responses in health and disease. We review metabolic pathways that influence immune cell function and cellular persistence in chronic infections. We also highlight the role of nutrients in altering the tissue microenvironment with lessons from the tumor microenvironment that shapes the quality and quantity of cellular immune responses. Multiple layers of biological networks, including the nature of nutritional supplements, the genetic background, previous exposures, and gut microbiota status have impact on cellular performance and immune competence against molecularly defined targets. We discuss how immune metabolism determines the differentiation pathway of antigen-specific immune cells and how these insights can be explored to devise better strategies to strengthen anti-pathogen-directed immune responses, while curbing unwanted, non-productive inflammation.

Gut microbiota in surgical and critically ill patients

Microbiota-defined as a collection of microbial organisms colonising different parts of the human body-is now recognised as a pivotal element of human health, and explains a large part of the variance in the phenotypic expression of many diseases. A reduction in microbiota diversity, and replacement of normal microbes with non-commensal, pathogenic or more virulent microbes in the gastrointestinal tract-also known as gut dysbiosis-is now considered to play a causal role in the pathogenesis of many acute and chronic diseases. Results from animal and human studies suggest that dysbiosis is linked to cardiovascular and metabolic disease through changes to microbiota-derived metabolites, including trimethylamine-N-oxide and short-chain fatty acids. Dysbiosis can occur within hours of surgery or the onset of critical illness, even without the administration of antibiotics. These pathological changes in microbiota may contribute to important clinical outcomes, including surgical infection, bowel anastomotic leaks, acute kidney injury, respiratory failure and brain injury. As a strategy to reduce dysbiosis, the use of probiotics (live bacterial cultures that confer health benefits) or synbiotics (probiotic in combination with food that encourages the growth of gut commensal bacteria) in surgical and critically ill patients has been increasingly reported to confer important clinical benefits, including a reduction in ventilator-associated pneumonia, bacteraemia and length of hospital stay, in small randomised controlled trials. However, the best strategy to modulate dysbiosis or counteract its potential harms remains uncertain and requires investigation by a well-designed, adequately powered, randomised controlled trial.

The Gut Microbiota and Its Implication in the Development of Atherosclerosis and Related Cardiovascular Diseases

The importance of gut microbiota in health and disease is being highlighted by numerous research groups worldwide. Atherosclerosis, the leading cause of heart disease and stroke, is responsible for about 50% of all cardiovascular deaths. Recently, gut dysbiosis has been identified as a remarkable factor to be considered in the pathogenesis of cardiovascular diseases (CVDs). In this review, we briefly discuss how external factors such as dietary and physical activity habits influence host-microbiota and atherogenesis, the potential mechanisms of the influence of gut microbiota in host blood pressure and the alterations in the prevalence of those bacterial genera affecting vascular tone and the development of hypertension. We will also be examining the microbiota as a therapeutic target in the prevention of CVDs and the beneficial mechanisms of probiotic administration related to cardiovascular risks. All these new insights might lead to novel analysis and CVD therapeutics based on the microbiota.

Ameliorative activity of Adansonia digitata fruit on high sugar/high fat diet-simulated Metabolic Syndrome model in male Wistar rats

Metabolic syndrome is a complex of metabolic disorders characterized by oxidative stress which compromises cell functions and entails multiple organs pathologies. We investigated the therapeutic and protective potential of Adansonia digitata fruit -a potent antioxidant- in high sugar/high fat diet-simulated metabolic syndrome in Wistar rats. 42 male rats (140-200 g) were randomly divided into 7 groups. G1 was kept on standard laboratory diet (SLD) for all 9 weeks (negative control). 5 groups were fed high Sugar/high fat diet for 6 weeks then switched to SLD for another 3 weeks + oral treatment as follows: G2+ no treatment (positive control), G3-G5 + 200, 400 and 800 mg/kg/day aqueous A. digitata fruit respectively, G6 + 10 mg/kg/day Simvastatin. G7 + HS/HFD + 400 mg/kg/day A. digitata fruit simultaneously and was terminated at W6. Our results showed that G2-G6 develops dyslipidemia, hyperglycaemia, weight gain, elevated hepatic biomarkers, elevated creatinine and urea plus pathological derangements in the heart, liver and kidney tissues compared to negative control at W6. 200 mg/kg/day A. digitata fruit significantly ameliorated the induced dyslipidemia (P ≤ 0.001), hyperglycaemia (P ≤ 0.001) with a significant reduction in the Atherogenic Index of Plasma (P ≤ 0.000) after 3 weeks treatment. The fruit normalized the elevated hepatic biomarkers as well as creatinine and urea. A dose dependent partial reduction in lesion intensity was observed in the hepatic tissue while the heart and kidney showed mostly reversed to normal histology. The inflammatory infiltration was eliminated. Relevant results were observed for the two higher doses. The simultaneous treatment showed significant lower levels in all biomarkers investigated compared to positive control which could be interpreted as protective activity. A reduction of 4-11% in whole body weight was achieved. CONCLUSION: MetS was successfully simulated with a HS/HFD formula in male Wistar rats. Treatment with aqueous A. digitata fruit showed anti-Metabolic Syndrome potential reflected by weight loss, anti-inflammatory, hypolipidemic, hypoglycaemic, renal, hepatic and cardio-protective activities.

Effect of probiotics on obesity-related markers per enterotype: a double-blind, placebo-controlled, randomized clinical trial

Background

Prevention and improvement of disease symptoms are important issues, and probiotics are suggested as a good treatment for controlling the obesity. Human gut microbiota has different community structures. Because gut microbial composition is assumed to be linked to probiotic function, this study evaluated the efficacy of probiotics on obesity-related clinical markers according to gut microbial enterotype.

Methods

Fifty subjects with body mass index over 25 kg/m² were randomly assigned to either the probiotic or placebo group. Each group received either unlabeled placebo or probiotic capsules for 12 weeks. Body weight, waist circumference, and body composition were measured every 3 weeks. Using computed tomography, total abdominal fat area and visceral fat area were measured. Blood and fecal samples were collected before and after the intervention for biochemical parameters and gut microbial compositions analysis.

Results

Gut microbial compositions of all the subjects were classified into two enterotypes according to Prevotella/Bacteroides ratio. The fat percentage, blood glucose, and insulin significantly increased in the Prevotella-rich enterotype of the placebo group. The obesity-related markers, such as waist circumference, total fat area, visceral fat, and ratio of visceral to subcutaneous fat area, were significantly reduced in the probiotic group. The decrease of obesity-related markers was greater in the Prevotella-rich enterotype than in the Bacteroides-rich enterotype.

Conclusion

Administration of probiotics improved obesity-related markers in obese people, and the efficacy of probiotics differed per gut microbial enterotype and greater responses were observed in the Prevotella-dominant enterotype.

A Review of the Role of the Gut Microbiome in Personalized Sports Nutrition

The gut microbiome is a key factor in determining inter-individual variability in response to diet. Thus, far, research in this area has focused on metabolic health outcomes such as obesity and type 2 diabetes. However, understanding the role of the gut microbiome in determining response to diet may also lead to improved personalization of sports nutrition for athletic performance. The gut microbiome has been shown to modify the effect of both diet and exercise, making it relevant to the athlete's pursuit of optimal performance. This area of research can benefit from recent developments in the general field of personalized nutrition and has the potential to expand our knowledge of the nexus between the gut microbiome, lifestyle, and individual physiology.

Epigenetic studies and pediatric research

The 2020 Annual Review Issue, "Preventing Disease in the 21st Century" was selected by the Editors-in-Chief of Pediatric Research to include a variety of disease entities that confront health-care practitioners entrusted to the care of infants and children. In keeping with this mandate, this article reviews the subject of epigenetics, which impacts pediatric research from bench to bedside. Epigenetic mechanisms exert their effects through the interaction of environment, various susceptibility genes, and immunologic development and include: (1) DNA methylation; (2) posttranslational modifications of histone proteins through acetylation and methylation, and (3) RNA-mediated gene silencing by microRNA (miRNA) regulation. The effects of epigenetics during fetal life and early periods of development are first reviewed together with clinical applications of cardiovascular and metabolic disorders in later life. The relationships of epigenetics to the allergic and autoimmune diseases and cancer are next reviewed. A specific focus of the article is directed to the recent recognition that many of these disorders are driven by aberrant immune responses in which immunoregulatory events are often poorly functioning and where through interventive epigenetic measures prevention may be possible by alterations in programming of DNA during fetal and early periods as well as in later life.