Dietary pattern and diversity analysis using DietDiveR in R: a cross-sectional evaluation in the National Health and Nutrition Examination Survey.

BACKGROUND: There are few resources available for researchers aiming to conduct 24-h dietary record and recall analysis using R. OBJECTIVES: We aimed to develop DietDiveR, which is a toolkit of functions written in R for the analysis of recall or record data collected with the Automated Self-Administered 24-h Dietary Assessment Tool or 2-d 24-h dietary recall data from the National Health and Nutrition Examination Survey (NHANES). The R functions are intended for food and nutrition researchers who are not computational experts. METHODS: DietDiveR provides users with functions to 1) clean dietary data, 2) analyze 24-h dietary intakes in relation to other study-specific metadata variables, 3) visualize percentages of energy intake from macronutrients, 4) perform principal component analysis or k-means clustering to group participants by similar data-driven dietary patterns, 5) generate foodtrees based on the hierarchical food group information for food items consumed, 6) perform principal coordinate analysis taking food grouping information into account, and 7) calculate diversity metrics for overall diet and specific food groups. DietDiveR includes a self-paced tutorial on a website (https://computational-nutrition-lab.github.io/DietDiveR/). As a demonstration, we applied DietDiveR to a demonstration data set and data from NHANES 2015-2016 to derive a dietary diversity measure of nuts, seeds, and legumes consumption. RESULTS: Adult participants in the NHANES 2015-2016 cycle were grouped depending on the diversity in their mean consumption of nuts, seeds, and legumes. The group with the highest diversity in nuts, seeds, and legumes consumption had 3.8 cm lower waist circumference (95% confidence interval: 1.0, 6.5) than those who did not consume nuts, seeds, and legumes. CONCLUSIONS: DietDiveR enables users to visualize dietary data and conduct data-driven dietary pattern analyses using R to answer research questions regarding diet. As a demonstration of this toolkit, we explored the diversity of nuts, seeds, and legumes consumption to highlight some of the ways DietDiveR can be used for analyses of dietary diversity.

Diet-induced shifts in the gut microbiota influence anastomotic healing in a murine model of colonic surgery.

Host diet and gut microbiota interact to contribute to perioperative complications, including anastomotic leak (AL). Using a murine surgical model of colonic anastomosis, we investigated how diet and fecal microbial transplantation (FMT) impacted the intestinal microbiota and if a predictive signature for AL could be determined. We hypothesized that a Western diet (WD) would impact gut microbial composition and that the resulting dysbiosis would correlate with increased rates of AL, while FMT from healthy, lean diet (LD) donors would reduce the risk of AL. Furthermore, we predicted that surgical outcomes would allow for the development of a microbial preclinical translational tool to identify AL. Here, we show that AL is associated with a dysbiotic microbial community characterized by increased levels of Bacteroides and Akkermansia. We identified several key taxa that were associated with leak formation, and developed an index based on the ratio of bacteria associated with the absence and presence of leak. We also highlight a modifiable connection between diet, microbiota, and anastomotic healing, potentially paving the way for perioperative modulation by microbiota-targeted therapeutics to reduce AL.

Bacterial, fungal, and interkingdom microbiome features of exclusively breastfeeding dyads are associated with infant age, antibiotic exposure, and birth mode.

The composition and function of early life gut bacterial communities (microbiomes) have been proposed to modulate health for the long term. In addition to bacteria, fungi (mycobiomes) also colonize the early life gut and have been implicated in health disorders such as asthma and obesity. Despite the potential importance of mycobiomes in health, there has been a lack of study regarding fungi and their interkingdom interactions with bacteria during infancy. The goal of this study was to obtain a more complete understanding of microbial communities thought to be relevant for the early life programming of health. Breastmilk and infant feces were obtained from a unique cohort of healthy, exclusively breastfeeding dyads recruited as part of the Mothers and Infants Linked for Healthy Growth (MILk) study with microbial taxa characterized using amplicon-based sequencing approaches. Bacterial and fungal communities in breastmilk were both distinct from those of infant feces, consistent with niche-specific microbial community development. Nevertheless, overlap was observed among sample types (breastmilk, 1-month feces, 6-month feces) with respect to the taxa that were the most prevalent and abundant. Self-reported antibacterial antibiotic exposure was associated with micro- as well as mycobiome variation, which depended upon the subject receiving antibiotics (mother or infant), timing of exposure (prenatal, peri- or postpartum), and sample type. In addition, birth mode was associated with bacterial and fungal community variation in infant feces, but not breastmilk. Correlations between bacterial and fungal taxa abundances were identified in all sample types. For infant feces, congruency between bacterial and fungal communities was higher for older infants, consistent with the idea of co-maturation of bacterial and fungal gut communities. Interkingdom connectedness also tended to be higher in older infants. Additionally, higher interkingdom connectedness was associated with Cesarean section birth and with antibiotic exposure for microbial communities of both breastmilk and infant feces. Overall, these results implicate infant age, birth mode, and antibiotic exposure in bacterial, fungal and interkingdom relationship variation in early-life-relevant microbiomes, expanding the current literature beyond bacteria.

Enhancing untargeted metabolomics using metadata-based source annotation.

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.

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.

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.

Characterisation, procedures and heritability of acute dietary intake in the Twins UK cohort: an observational study.

BACKGROUND: Estimated food records (EFR) are a common dietary assessment method. This investigation aimed to; (1) define the reporting quality of the EFR, (2) characterise acute dietary intake and eating behaviours, (3) describe diet heritability. METHODS: A total of 1974 one-day EFR were collected from 1858 participants in the TwinsUK cohort between 2012 and 2017. EFR were assessed using a six-point scoring system to determine reporting quality. The frequency and co-occurrence of food items was examined using word clouds and co-occurrence networks. The impact of eating behaviours on weight, BMI and nutrient intake were explored using mixed-effect linear regression models. Finally, diet heritability was estimated using ACE modelling. RESULTS: We observed that 75% of EFR are of acceptable reporting quality (score > 5). Black tea and semi-skimmed milk were the most consumed items, on an individual basis (respectively 8.27, 6.25%) and paired (0.21%) as co-occurring items. Breakfast consumption had a significantly (p = 5.99 × 10- 7) greater impact on energy (kcal) (mean 1874.67 (±SD 532.42)) than skipping breakfast (1700.45 (±SD 620.98)), however only length of eating window was significantly associated with body weight (kg) (effect size 0.21 (±SD 0.10), p = 0.05) and BMI (effect size 0.08 (±SD 0.04), p = 0.04) after adjustment for relevant covariates. Lastly, we reported that both length of eating window (h2 = 33%, CI 0.24; 0.41), and breakfast consumption (h2 = 11%, CI 0.02; 0.21) were weakly heritable. CONCLUSIONS: EFR describing acute dietary intake allow for eating behaviour characterisation and can supplement habitual diet intake assessments. Novel findings of heritability warrant further investigation.

Perspective: A Framework for Addressing Dynamic Food Consumption Processes.

The study of food consumption, diet, and related concepts is motivated by diverse goals, including understanding why food consumption impacts our health, and why we eat the foods we do. These varied motivations can make it challenging to define and measure consumption, as it can be specified across nearly infinite dimensions-from micronutrients to carbon footprint to food preparation. This challenge is amplified by the dynamic nature of food consumption processes, with the underlying phenomena of interest often based on the nature of repeated interactions with food occurring over time. This complexity underscores a need to not only improve how we measure food consumption but is also a call to support theoreticians in better specifying what, how, and why food consumption occurs as part of processes, as a prerequisite step to rigorous measurement. The purpose of this Perspective article is to offer a framework, the consumption process framework, as a tool that researchers in a theoretician role can use to support these more robust definitions of consumption processes. In doing so, the framework invites theoreticians to be a bridge between practitioners who wish to measure various aspects of food consumption and methodologists who can develop measurement protocols and technologies that can support measurement when consumption processes are clearly defined. In the paper we justify the need for such a framework, introduce the consumption process framework, illustrate the framework via a use case, and discuss existing technologies that enable the use of this framework and, by extension, more rigorous study of consumption. This consumption process framework demonstrates how theoreticians could fundamentally shift how food consumption is defined and measured towards more rigorous study of what, how, and why food is eaten as part of dynamic processes and a deeper understanding of linkages between behavior, food, and health.

Dietary diversity contributes to microbiome associations in autism.

Research exploring the links between the microbiome and autism has inadequately considered the contribution of diet diversity. Recently in Cell, Yap et al. addressed the contribution of restrictive dietary patterns to microbiome diversity in autism and found that decreased dietary diversity shapes the microbiome more than previously appreciated.

Does caloric restriction prime the microbiome for pathogenic bacteria?

A recent study in Nature finds significant shifts in the gut microbiome of post-menopausal women on severe calorie restriction concomitant with weight loss. Microbiota transplantation to germ-free mice display a similar weight loss, along with a bloom in C. difficile, unlocking clues to microbial metabolic alteration in weight loss.

Saliva Testing Is Accurate for Early-Stage and Presymptomatic COVID-19.

Although nasopharyngeal samples have been considered the gold standard for COVID-19 testing, variability in viral load across different anatomical sites could cause nasopharyngeal samples to be less sensitive than saliva or nasal samples in certain cases. Self-collected samples have logistical advantages over nasopharyngeal samples, making them amenable to population-scale screening. To evaluate sampling alternatives for population screening, we collected nasopharyngeal, saliva, and nasal samples from two cohorts with varied levels and types of symptoms. In a mixed cohort of 60 symptomatic and asymptomatic participants, we found that saliva had 88% concordance with nasopharyngeal samples when tested in the same testing lab (n = 41) and 68% concordance when tested in different testing labs (n = 19). In a second cohort of 20 participants hospitalized for COVID-19, saliva had 74% concordance with nasopharyngeal samples tested in the same testing lab but detected virus in two participants that tested negative with nasopharyngeal samples on the same day. Medical record review showed that the saliva-based testing sensitivity was related to the timing of symptom onset and disease stage. We find that no sample site will be perfectly sensitive for COVID-19 testing in all situations, and the significance of negative results will always need to be determined in the context of clinical signs and symptoms. Saliva retained high clinical sensitivity for early-stage and presymptomatic COVID-19 while allowing easier collection, minimizing the exposure of health care workers, and need for personal protective equipment and making it a viable option for population-scale testing. IMPORTANCE Methods for COVID-19 detection are necessary for public health efforts to monitor the spread of disease. Nasopharyngeal samples have been considered the best approach for COVID-19 testing. However, alternative samples like self-collected saliva offer advantages for population-scale screening. Meta-analyses of recent studies suggest that saliva is useful for detecting SARS-CoV-2; however, differences in disease prevalence, sample collection, and analysis methods still confound strong conclusions on the utility of saliva compared to nasopharyngeal samples. Here, we find that the sensitivity of saliva testing is related to both the timing of the sample collection relative to symptom onset and the disease stage. Importantly, several clinical vignettes in our cohorts highlight the challenges of medical decision making with limited knowledge of the associations between laboratory test data and the natural biology of infection.

Multi-Omics Analyses Show Disease, Diet, and Transcriptome Interactions With the Virome.

BACKGROUND & AIMS: The gut virome includes eukaryotic viruses and bacteriophages that can shape the gut bacterial community and elicit host responses. The virome can be implicated in diseases, such as irritable bowel syndrome (IBS), where gut bacteria play an important role in pathogenesis. We provide a comprehensive and longitudinal characterization of the virome, including DNA and RNA viruses and paired multi-omics data in a cohort of healthy subjects and patients with IBS. METHODS: We selected 2 consecutive stool samples per subject from a longitudinal study cohort and performed metagenomic sequencing on DNA and RNA viruses after enriching for viral-like particles. Viral sequence abundance was evaluated over time, as well as in the context of diet, bacterial composition and function, metabolite levels, colonic gene expression, host genetics, and IBS subsets. RESULTS: We found that the gut virome was temporally stable and correlated with the colonic transcriptome. We identified IBS-subset-specific changes in phage populations; Microviridae, Myoviridae, and Podoviridae species were elevated in diarrhea-predominant IBS, and other Microviridae and Myoviridae species were elevated in constipation-predominant IBS compared to healthy controls. We identified correlations between subsets of the virome and bacterial composition (unclassifiable "dark matter" and phages) and diet (eukaryotic viruses). CONCLUSIONS: We found that the gut virome is stable over time but varies among subsets of patients with IBS. It can be affected by diet and potentially influences host function via interactions with gut bacteria and/or altering host gene expression.

Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome.

The gut microbiome has been implicated in multiple human chronic gastrointestinal (GI) disorders. Determining its mechanistic role in disease has been difficult due to apparent disconnects between animal and human studies and lack of an integrated multi-omics view of disease-specific physiological changes. We integrated longitudinal multi-omics data from the gut microbiome, metabolome, host epigenome, and transcriptome in the context of irritable bowel syndrome (IBS) host physiology. We identified IBS subtype-specific and symptom-related variation in microbial composition and function. A subset of identified changes in microbial metabolites correspond to host physiological mechanisms that are relevant to IBS. By integrating multiple data layers, we identified purine metabolism as a novel host-microbial metabolic pathway in IBS with translational potential. Our study highlights the importance of longitudinal sampling and integrating complementary multi-omics data to identify functional mechanisms that can serve as therapeutic targets in a comprehensive treatment strategy for chronic GI diseases. VIDEO ABSTRACT.

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.

Wild primate microbiomes prevent weight gain in germ-free mice.

BACKGROUND: The gut microbiome harbors trillions of bacteria that play a major role in dietary nutrient extraction and host metabolism. Metabolic diseases such as obesity and diabetes are associated with shifts in microbiome composition and have been on the rise in Westernized or highly industrialized countries. At the same time, Westernized diets low in dietary fiber have been shown to cause loss of gut microbial diversity. However, the link between microbiome composition, loss of dietary fiber, and obesity has not been well defined. RESULTS: To study the interactions between gut microbiota, dietary fiber, and weight gain, we transplanted captive and wild douc gut microbiota into germ-free mice and then exposed them to either a high- or low-fiber diet. The group receiving captive douc microbiota gained significantly more weight, regardless of diet, while mice receiving a high-fiber diet and wild douc microbiota remained lean. In the presence of a low-fiber diet, the wild douc microbiota partially prevented weight gain. Using 16S rRNA gene amplicon sequencing we identified key bacterial taxa in each group, specifically a high relative abundance of Bacteroides and Akkermansia in captive douc FMT mice and a higher relative abundance of Lactobacillus and Clostridium in the wild douc FMT mice. CONCLUSIONS: In the context of our germ-free mouse experiment, wild douc microbiota could serve as a reservoir for microbes for cross-species transplants. Our results suggest that wild douc microbiota are tailored to diverse fiber diets and can prevent weight gain when exposed to a native diet.

Use of Exclusive Enteral Formula Diet as Adjunctive Therapy for Treatment of a Crohn's Disease Flare.

Introduction: We report the case of an adult patient who achieved remission of a Crohn's disease flare after treatment with exclusive enteral nutrition as adjunctive therapy to medication. Case Report: A 46-year-old man with severe, stricturing Crohn's presented for severe abdominal pain and weight loss; estimated Crohn's Disease Activity Index score greater than 300. Antibiotics, vedolizumab, budesonide, and exclusive enteral nutrition diet were instituted. Approximately 30 days later, his Crohn's Disease Activity Index score improved to 170. Discussion: This case illustrates the possible utility of an exclusive enteral formula diet as an adjunct to medication to induce remission of a Crohn's disease flare.