Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults

Metaproteomics and DNA metabarcoding as tools to assess dietary intake in humans

Objective biomarkers of food intake are a sought-after goal in nutrition research. Most biomarker development to date has focused on metabolites detected in blood, urine, skin or hair, but detection of consumed foods in stool has also been shown to be possible via DNA sequencing. An additional food macromolecule in stool that harbors sequence information is protein. However, the use of protein as an intake biomarker has only been explored to a very limited extent. Here, we evaluate and compare measurement of residual food-derived DNA and protein in stool as potential biomarkers of intake. We performed a pilot study of DNA sequencing-based metabarcoding (FoodSeq) and mass spectrometry-based metaproteomics in five individuals' stool sampled in short, longitudinal bursts accompanied by detailed diet records (n=27 total samples). Dietary data provided by stool DNA, stool protein, and written diet record independently identified a strong within-person dietary signature, identified similar food taxa, and had significantly similar global structure in two of the three pairwise comparisons between measurement techniques (DNA-to-protein and DNA-to-diet record). Metaproteomics identified proteins including myosin, ovalbumin, and beta-lactoglobulin that differentiated food tissue types like beef from dairy and chicken from egg, distinctions that were not possible by DNA alone. Overall, our results lay the groundwork for development of targeted metaproteomic assays for dietary assessment and demonstrate that diverse molecular components of food can be leveraged to study food intake using stool samples.

Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments

Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach --- Microbiome-based nutrient profile corrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.

Fecal Metagenomics to Identify Biomarkers of Food Intake in Healthy Adults: Findings from Randomized, Controlled, Nutrition Trials

BACKGROUND

Undigested components of the human diet affect the composition and function of the microorganisms present in the gastrointestinal tract. Techniques like metagenomic analyses allow researchers to study functional capacity, thus revealing the potential of using metagenomic data for developing objective biomarkers of food intake.

OBJECTIVES

As a continuation of our previous work using 16S and metabolomic datasets, we aimed to utilize a computationally intensive, multivariate, machine-learning approach to identify fecal KEGG (Kyoto encyclopedia of genes and genomes) Orthology (KO) categories as biomarkers that accurately classify food intake.

METHODS

Data were aggregated from 5 controlled feeding studies that studied the individual impact of almonds, avocados, broccoli, walnuts, barley, and oats on the adult gastrointestinal microbiota. Deoxyribonucleic acid from preintervention and postintervention fecal samples underwent shotgun genomic sequencing. After preprocessing, sequences were aligned and functionally annotated with Double Index AlignMent Of Next-generation sequencing Data v2.0.11.149 and MEtaGenome ANalyzer v6.12.2, respectively. After the count normalization, the log of the fold change ratio for resulting KOs between pre- and postintervention of the treatment group against its corresponding control was utilized to conduct differential abundance analysis. Differentially abundant KOs were used to train machine-learning models examining potential biomarkers in both single-food and multi-food models.

RESULTS

We identified differentially abundant KOs in the almond (n = 54), broccoli (n = 2474), and walnut (n = 732) groups (q < 0.20), which demonstrated classification accuracies of 80%, 87%, and 86% for the almond, broccoli, and walnut groups using a random forest model to classify food intake into each food group's respective treatment and control arms, respectively. The mixed-food random forest achieved 81% accuracy.

CONCLUSIONS

Our findings reveal promise in utilizing fecal metagenomics to objectively complement self-reported measures of food intake. Future research on various foods and dietary patterns will expand these exploratory analyses for eventual use in feeding study compliance and clinical settings.

Diet Quality and the Fecal Microbiota in Adults in the American Gut Project

BACKGROUND

The Dietary Guidelines for Americans advises on dietary intake to meet nutritional needs, promote health, and prevent diseases. Diet affects the intestinal microbiota and is increasingly linked to health. It is vital to investigate the relationships between diet quality and the microbiota to better understand the impact of nutrition on human health.

OBJECTIVES

This study aimed to investigate the differences in fecal microbiota composition in adults from the American Gut Project based on their adherence to the Dietary Guidelines for Americans.

METHODS

This study was a cross-sectional analysis of the 16S sequencing and food frequency data of a subset of adults (n = 432; age = 18-60 y; 65% female, 89% white) participating in the crowdsourced American Gut Project. The Healthy Eating Index-2015 assessed the compliance with Dietary Guideline recommendations. The cohort was divided into tertiles based on Healthy Eating Index-2015 scores, and differences in taxonomic abundances and diversity were compared between high and low scorers.

RESULTS

The mean Total Score for low-scoring adults (58.1 ± 5.4) was comparable with the reported score of the average American adult (56.7). High scorers for the Total Score and components related to vegetables, grains, and dairy had greater alpha diversity than low scorers. High scorers in the fatty acid component had a lower alpha diversity than low scorers (95% CI: 0.35, 1.85). A positive log-fold difference in abundance of plant carbohydrate-metabolizing taxa in the families Lachnospiraceae and Ruminococcaceae was observed in high-scoring tertiles for Total Score, vegetable, fruit, and grain components (Benjamini-Hochberg; q < 0.05).

CONCLUSIONS

Adults with greater compliance to the Dietary Guidelines demonstrated higher diversity in their fecal microbiota and greater abundance of bacteria capable of metabolizing complex carbohydrates, providing evidence on how Dietary Guidelines support the gut microbiota.

Strategies to Assess the Impact of Sustainable Functional Food Ingredients on Gut Microbiota

Nowadays, it is evident that food ingredients have different roles and distinct health benefits to the consumer. Over the past years, the interest in functional foods, especially those targeting gut health, has grown significantly. The use of industrial byproducts as a source of new functional and sustainable ingredients as a response to such demands has raised interest. However, the properties of these ingredients can be affected once incorporated into different food matrices. Therefore, when searching for the least costly and most suitable, beneficial, and sustainable formulations, it is necessary to understand how such ingredients perform when supplemented in different food matrices and how they impact the host's health. As proposed in this manuscript, the ingredients' properties can be first evaluated using in vitro gastrointestinal tract (GIT) simulation models prior to validation through human clinical trials. In vitro models are powerful tools that mimic the physicochemical and physiological conditions of the GIT, enabling prediction of the potentials of functional ingredients per se and when incorporated into a food matrix. Understanding how newly developed ingredients from undervalued agro-industrial sources behave as supplements supports the development of new and more sustainable functional foods while scientifically backing up health-benefits claims.

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.

Validating dietary assessment tools with energy expenditure measurement methods: Is this accurate?

Having an accurate dietary assessment tool is a necessity for most nutritional studies. As a result, many validation studies have been carried out to assess the validity of commonly used dietary assessment tools. Since based on the energy balance equation, among individuals with a stable weight, Energy Intake (EI) is equal to Energy Expenditure (EE) and there are precise methods for measurement of EE (e.g. doubly labeled water method), numerous studies have used this technique for validating dietary assessment tools. If there was a discrepancy between measured EI and EE, the researchers have concluded that self-reported dietary assessment tools are not valid or participants misreport their dietary intakes. However, the calculation of EI with common dietary assessment tools such as food frequency questionnaires (FFQs), 24-hour dietary recalls, or weighed food records, is based on fixed factors that were introduced by Atwater and the accuracy of these factors are under question. Moreover, the amount of energy absorption, and utilization from a diet, depends on various factors and there are considerable interindividual differences in this regard, for example in gut microbiota composition. As a result, the EI which is calculated using dietary assessment tools is likely not representative of real metabolizable energy which is equal to EE in individuals with stable weight, thus validating dietary assessment tools with EE measurement methods may not be accurate. We aim to address this issue briefly and propose a feasible elucidation, albeit not a complete solution.

Fecal Metabolites as Biomarkers for Predicting Food Intake by Healthy Adults

BACKGROUND

The fecal metabolome is affected by diet and includes metabolites generated by human and microbial metabolism. Advances in -omics technologies and analytic approaches have allowed researchers to identify metabolites and better utilize large data sets to generate usable information. One promising aspect of these advancements is the ability to determine objective biomarkers of food intake.

OBJECTIVES

We aimed to utilize a multivariate, machine learning approach to identify metabolite biomarkers that accurately predict food intake.

METHODS

Data were aggregated from 5 controlled feeding studies in adults that tested the impact of specific foods (almonds, avocados, broccoli, walnuts, barley, and oats) on the gastrointestinal microbiota. Fecal samples underwent GC-MS metabolomic analysis; 344 metabolites were detected in preintervention samples, whereas 307 metabolites were detected postintervention. After removing metabolites that were only detected in either pre- or postintervention and those undetectable in ≥80% of samples in all study groups, changes in 96 metabolites relative concentrations (treatment postintervention minus preintervention) were utilized in random forest models to 1) examine the relation between food consumption and fecal metabolome changes and 2) rank the fecal metabolites by their predictive power (i.e., feature importance score).

RESULTS

Using the change in relative concentration of 96 fecal metabolites, 6 single-food random forest models for almond, avocado, broccoli, walnuts, whole-grain barley, and whole-grain oats revealed prediction accuracies between 47% and 89%. When comparing foods with one another, almond intake was differentiated from walnut intake with 91% classification accuracy.

CONCLUSIONS

Our findings reveal promise in utilizing fecal metabolites as objective complements to certain self-reported food intake estimates. Future research on other foods at different doses and dietary patterns is needed to identify biomarkers that can be applied in feeding study compliance and clinical settings.

Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease

This study reviews the recent progress of explainable artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The source of data was eight original studies in PubMed. The search terms were "gastrointestinal" (title) together with "random forest" or "explainable artificial intelligence" (abstract). The eligibility criteria were the dependent variable of GID or a strongly associated disease, the intervention(s) of artificial intelligence, the outcome(s) of accuracy and/or the area under the receiver operating characteristic curve (AUC), the outcome(s) of variable importance and/or the Shapley additive explanations (SHAP), a publication year of 2020 or later, and the publication language of English. The ranges of performance measures were reported to be 0.70-0.98 for accuracy, 0.04-0.25 for sensitivity, and 0.54-0.94 for the AUC. The following factors were discovered to be top-10 predictors of gastrointestinal bleeding in the intensive care unit: mean arterial pressure (max), bicarbonate (min), creatinine (max), PMN, heart rate (mean), Glasgow Coma Scale, age, respiratory rate (mean), prothrombin time (max) and aminotransferase aspartate (max). In a similar vein, the following variables were found to be top-10 predictors for the intake of almond, avocado, broccoli, walnut, whole-grain barley, and/or whole-grain oat: Roseburia undefined, Lachnospira spp., Oscillibacter undefined, Subdoligranulum spp., Streptococcus salivarius subsp. thermophiles, Parabacteroides distasonis, Roseburia spp., Anaerostipes spp., Lachnospiraceae ND3007 group undefined, and Ruminiclostridium spp. Explainable artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of GID.

Herbs and Spices Modulate Gut Bacterial Composition in Adults at Risk for CVD: Results of a Prespecified Exploratory Analysis from a Randomized, Crossover, Controlled-Feeding Study

BACKGROUND

Herbs and spices are rich in polyphenolic compounds that may influence gut bacterial composition. The effect of culinary doses of herbs and spices consumed as part of a well-defined dietary pattern on gut bacterial composition has not been previously studied.

OBJECTIVES

The aim of this prespecified exploratory analysis was to examine gut bacterial composition following an average American diet (carbohydrate: 50% kcal; protein: 17%; total fat: 33%; saturated fat: 11%) containing herbs and spices at 0.5, 3.3, and 6.6 g.d^-1.2100 kcal^-1 [low-, moderate-, and high-spice diets, respectively (LSD, MSD, and HSD)] in adults at risk for CVD.

METHODS

Fifty-four adults (57% female; mean ± SD age: 45 ± 11 y; BMI: 29.8 ± 2.9 kg/m²; waist circumference: 102.8 ± 7.1 cm) were included in this 3-period, randomized, crossover, controlled-feeding study. Each diet was provided for 4 wk with a minimum 2-wk washout period. At baseline and the end of each diet period, participants provided a fecal sample for 16S rRNA gene (V4 region) sequencing. QIIME2 was used for data filtration, sequence clustering, taxonomy assignment, and statistical analysis.

RESULTS

α-diversity assessed by the observed features metric ( P = 0.046) was significantly greater following the MSD as compared with the LSD; no other between-diet differences in α-diversity were detected. Differences in β-diversity were not observed between the diets ( P = 0.45). Compared with baseline, β-diversity differed following all diets ( P < .02). Enrichment of the Ruminococcaceae family was observed following the HSD as compared with the MSD (relative abundance = 22.14%, linear discriminant analysis = 4.22, P = 0.03) and the LSD (relative abundance = 24.90%, linear discriminant analysis = 4.47, P = 0.004).

CONCLUSIONS

The addition of herbs and spices to an average American diet induced shifts in gut bacterial composition after 4 wk in adults at risk for CVD. The metabolic implications of these changes merit further investigation. This trial was registered at clinicaltrials.gov as NCT03064932.

A Guide to Dietary Pattern-Microbiome Data Integration

The human gut microbiome is linked to metabolic and cardiovascular disease risk. Dietary modulation of the human gut microbiome offers an attractive pathway to manipulate the microbiome to prevent microbiome-related disease. However, this promise has not been realized. The complex system of diet and microbiome interactions is poorly understood. Integrating observational human diet and microbiome data can help researchers and clinicians untangle the complex systems of interactions that predict how the microbiome will change in response to foods. The use of dietary patterns to assess diet-microbiome relations holds promise to identify interesting associations and result in findings that can directly translate into actionable dietary intake recommendations and eating plans. In this article, we first highlight the complexity inherent in both dietary and microbiome data and introduce the approaches generally used to explore diet and microbiome simultaneously in observational studies. Second, we review the food group and dietary pattern-microbiome literature focusing on dietary complexity-moving beyond nutrients. Our review identified a substantial and growing body of literature that explores links between the microbiome and dietary patterns. However, there was very little standardization of dietary collection and assessment methods across studies. The 54 studies identified in this review used ≥7 different methods to assess diet. Coupled with the variation in final dietary parameters calculated from dietary data (e.g., dietary indices, dietary patterns, food groups, etc.), few studies with shared methods and assessment techniques were available for comparison. Third, we highlight the similarities between dietary and microbiome data structures and present the possibility that multivariate and compositional methods, developed initially for microbiome data, could have utility when applied to dietary data. Finally, we summarize the current state of the art for diet-microbiome data integration and highlight ways dietary data could be paired with microbiome data in future studies to improve the detection of diet-microbiome signals.

A starch- and sucrose-reduced dietary intervention in irritable bowel syndrome patients produced a shift in gut microbiota composition along with changes in phylum, genus, and amplicon sequence variant abundances, without affecting the micro-RNA levels

Background/Aim

A randomized clinical trial with a starch‐ and sucrose‐reduced diet (SSRD) in irritable bowel syndrome (IBS) patients has shown clear improvement of participants' symptoms. The present study aimed to explore the effects of the SSRD on the gut microbiota and circulating micro‐RNA in relation to nutrient intake and gastrointestinal symptoms.

Methods

IBS patients were randomized to a 4‐week SSRD intervention (n = 80) or control group (n = 25); habitual diet). At baseline and 4 weeks, blood and fecal samples, 4 day‐dietary records, and symptom questionnaires were collected, that is, Rome IV questionnaires, IBS‐symptom severity score (IBS‐SSS) and visual analog scale for IBS (VAS‐IBS). Micro‐RNA was analyzed in blood and microbiota in faeces by 16S rRNA from regions V1–V2.

Results

The alpha diversity was unaffected, whereas beta diversity was decreased (p < 0.001) along with increased abundance of Proteobacteria (p = 0.0036) and decreased abundance of Bacteroidetes phyla (p < 0.001) in the intervention group at 4 weeks. Few changes were noted in the controls. The shift in beta diversity and phyla abundance correlated with decreased intakes of carbohydrates, disaccharides, and starch and increased fat and protein intakes. Proteobacteria abundance also correlated positively (R² = 0.07, p = 0.0016), and Bacteroidetes negatively (R² = 0.07, p = 0.0017), with reduced total IBS‐SSS. Specific genera, for example, Eubacterium eligens, Lachnospiraceae UCG‐001, Victivallis, and Lachnospira increased significantly in the intervention group (p < 0.001 for all), whereas Marvinbryantia, DTU089 (Ruminoccocaceae family), Enterorhabdus, and Olsenella decreased, together with changes in amplicon sequence variant (ASV) levels. Modest changes of genus and ASV abundance were observed in the control group. No changes were observed in micro‐RNA expression in either group.

Conclusion

The SSRD induced a shift in beta diversity along with several bacteria at different levels, associated with changes in nutrient intakes and reduced gastrointestinal symptoms. No corresponding changes were observed in the control group. Neither the nutrient intake nor the microbiota changes affected micro‐RNA expression.

The study was registered at ClinicalTrials.gov data base (NCT03306381).

Can Supplemented Skim Milk (SKM) Boost Your Gut Health?

The incorporation of functional ingredients, such as prebiotics and probiotics in food matrices, became a common practice in the human diet to improve the nutritional value of the food product itself. Worldwide, skim milk (SKM) is one of the most consumed food matrices, comprising all the essential nutrients desired for a balanced diet. Thus, the modulation of the human gut microbiota by SKM supplemented with different well-known functional ingredients was evaluated. Four well-studied prebiotics, fructo-oligosaccharides (FOS), galacto-oligosaccharides (GOS), mannan-oligosaccharides (MOS) and inulin, and one probiotic product, UL-250® (Saccharomyces boulardii) were added at 1% (w/v) to SKM and subjected to a gastrointestinal in vitro model. The impact of each combination on gut microbiota profile and their fermentation metabolites (i.e., short-chain fatty acids–SCFA) was assessed by quantitative polymerase chain reaction (qPCR) and high-performance liquid chromatography (HPLC), respectively. The addition of FOS to SKM had promising results, showing prebiotic potential by promoting the growth of Lactobacillus, Bifidobacterium, and Clostridium cluster IV. Moreover, the increment of SCFA levels and the decrease of total ammonia nitrogen were observed throughout colonic fermentation. Overall, these results demonstrate that the combination SKM + FOS was the most beneficial to the host’s health by positively modulating the gut microbiota.

Validating dietary assessment tools with energy expenditure measurement methods: Is this accurate?

Abstract. Having an accurate dietary assessment tool is a necessity for most nutritional studies. As a result, many validation studies have been carried out to assess the validity of commonly used dietary assessment tools. Since based on the energy balance equation, among individuals with a stable weight, Energy Intake (EI) is equal to Energy Expenditure (EE) and there are precise methods for measurement of EE (e.g. doubly labeled water method), numerous studies have used this technique for validating dietary assessment tools. If there was a discrepancy between measured EI and EE, the researchers have concluded that self-reported dietary assessment tools are not valid or participants misreport their dietary intakes. However, the calculation of EI with common dietary assessment tools such as food frequency questionnaires (FFQs), 24-hour dietary recalls, or weighed food records, is based on fixed factors that were introduced by Atwater and the accuracy of these factors are under question. Moreover, the amount of energy absorption, and utilization from a diet, depends on various factors and there are considerable interindividual differences in this regard, for example in gut microbiota composition. As a result, the EI which is calculated using dietary assessment tools is likely not representative of real metabolizable energy which is equal to EE in individuals with stable weight, thus validating dietary assessment tools with EE measurement methods may not be accurate. We aim to address this issue briefly and propose a feasible elucidation, albeit not a complete solution.

Fueling Gut Microbes: A Review of the Interaction between Diet, Exercise, and the Gut Microbiota in Athletes

The athlete's goal is to optimize their performance. Towards this end, nutrition has been used to improve the health of athletes' brains, bones, muscles, and cardiovascular system. However, recent research suggests that the gut and its resident microbiota may also play a role in athlete health and performance. Therefore, athletes should consider dietary strategies in the context of their potential effects on the gut microbiota, including the impact of sports-centric dietary strategies (e.g., protein supplements, carbohydrate loading) on the gut microbiota as well as the effects of gut-centric dietary strategies (e.g., probiotics, prebiotics) on performance. This review provides an overview of the interaction between diet, exercise, and the gut microbiota, focusing on dietary strategies that may impact both the gut microbiota and athletic performance. Current evidence suggests that the gut microbiota could, in theory, contribute to the effects of dietary intake on athletic performance by influencing microbial metabolite production, gastrointestinal physiology, and immune modulation. Common dietary strategies such as high protein and simple carbohydrate intake, low fiber intake, and food avoidance may adversely impact the gut microbiota and, in turn, performance. Conversely, intake of adequate dietary fiber, a variety of protein sources, and emphasis on unsaturated fats, especially omega-3 (ɷ-3) fatty acids, in addition to consumption of prebiotics, probiotics, and synbiotics, have shown promising results in optimizing athlete health and performance. Ultimately, while this is an emerging and promising area of research, more studies are needed that incorporate, control, and manipulate all 3 of these elements (i.e., diet, exercise, and gut microbiome) to provide recommendations for athletes on how to "fuel their microbes."

Artificial intelligence in nutrition research: perspectives on current and future applications

Artificial intelligence (AI) is a rapidly evolving area that offers unparalleled opportunities of progress and applications in many healthcare fields. In this review, we provide an overview of the main and latest applications of AI in nutrition research and identify gaps to address to potentialize this emerging field. AI algorithms may help better understand and predict the complex and non-linear interactions between nutrition-related data and health outcomes, particularly when large amounts of data need to be structured and integrated, such as in metabolomics. AI-based approaches, including image recognition, may also improve dietary assessment by maximizing efficiency and addressing systematic and random errors associated with self-reported measurements of dietary intakes. Finally, AI applications can extract, structure and analyze large amounts of data from social media platforms to better understand dietary behaviours and perceptions among the population. In summary, AI-based approaches will likely improve and advance nutrition research as well as help explore new applications. However, further research is needed to identify areas where AI does deliver added value compared with traditional approaches, and other areas where AI is simply not likely to advance the field. Novelty: Artificial intelligence offers unparalleled opportunities of progress and applications in nutrition. There remain gaps to address to potentialize this emerging field.

Nutritional Interventions and the Gut Microbiome in Children

The gut microbiome plays an integral role in health and disease, and diet is a major driver of its composition, diversity, and functional capacity. Given the dynamic development of the gut microbiome in infants and children, it is critical to address two major questions: (a) Can diet modify the composition, diversity, or function of the gut microbiome, and (b) will such modification affect functional/clinical outcomes including immune function, cognitive development, and overall health? We synthesize the evidence on the effect of nutritional interventions on the gut microbiome in infants and children across 26 studies. Findings indicate the need to study older children, assess the whole intestinal tract, and harmonize methods and interpretation of findings, which are critical for informing meaningful clinical and public health practice. These findings are relevant for precision health, may help identify windows of opportunity for intervention, and may inform the design and delivery of such interventions. Expected final online publication date for the Annual Review of Nutrition, Volume 41 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.