Systematically assessing microbiome-disease associations identifies drivers of inconsistency in metagenomic research

Analysis of gill and skin microbiota in Larimichthys crocea reveals bacteria associated with cryptocaryoniasis resistance potential

Cryptocaryoniasis, caused by the ciliate parasite Cryptocaryon irritans, poses a significant threat to the large yellow croaker (Larimichthys crocea) in intensive marine aquaculture. This study explores the interaction between skin and gill microbiota and C. irritans infection, focusing on the role of commensal microbes in disease resistance. Fish were challenged with 100 theronts per gram of body weight, leading to substantial microbial dysbiosis, characterized by decreased alpha diversity and disrupted co-occurrence networks, particularly on the skin. Post-infection, Vibrio abundance significantly increased in both gills and skin, suggesting potential for secondary infections. Conversely, lower Vibrio levels correlated with higher populations of Bdellovibrio-like organisms (BALOs), which may play a beneficial role in microbial balance. Fish showed varying susceptibility, with mildly infected individuals exhibiting less histopathological damage and a stronger immune response, indicated by elevated interleukin-1β (IL-1β) and interleukin-8 (IL-8) levels. Correlation analyses revealed significant relationships between relative infection intensity (RII) and microbial composition, with certain bacteria known for anti-eukaryotic microbial properties showing negative correlations with RII. Additionally, the abundance of nitrogen-metabolizing bacteria also correlated negatively with RII. Functional predictions indicated increased bacterial genes related to denitrification and vitamin biosynthesis post-infection. Notably, Candidatus Midichloria was identified as a potential biomarker for C. irritans infection and is thought to be an endosymbiont of C. irritans, with its presence validated through PCR analysis. These findings illuminate microbial dynamics during C. irritans infection and suggest probiotic candidates for managing cryptocaryoniasis.

Restoration of intestinal secondary bile acid synthesis: A potential approach to improve pancreatic β cell function in type 1 diabetes

This study investigates the roles of gut microbiome and secondary bile acid dysfunctions in type 1 diabetes (T1D) and explores targeted interventions to address them. It finds that T1D is associated with reduced gut microbial diversity and imbalance favoring harmful bacteria over beneficial ones. Additionally, patients with T1D exhibited impaired secondary bile acid metabolism. Interventions aimed at modulating the gut microbiome and metabolites are safe and improve glycemic control, reduce daily insulin dose, and reduce inflammation. These interventions reshape the gut microbiome toward a healthier state and enhance secondary bile acid production. Responders to the interventions show increased levels of beneficial bacteria and secondary bile acids, along with improved C-peptide responses. Overall, these findings suggest that targeted modulation of the gut microbiome and secondary bile acid metabolism could be a promising therapeutic approach for T1D management. The trial is registered at Chinese Clinical Trial Registry (ChiCTR-ONN-17011279).

The role of the gut microbiome in the associations between lead exposure and child neurodevelopment

Lead is highly toxic to the developing brain. Given its persistence in the environment, new intervention strategies are needed to mitigate the impacts of lead on child neurodevelopment. The gut microbiome, referring to the bacteria and microorganisms residing in the gastrointestinal system, may be a viable target for intervention. This short review summarizes recent evidence linking the gut-brain axis to child developmental outcomes. We explore how lead-induced effects to the gut microbiome could indirectly affect child neurodevelopment, such that disrupting or offsetting this mediating process could buffer the effects of lead on child developmental outcomes. Unexpected findings with respect to child microbiota diversity and child cognitive and behavioral outcomes as well as lead exposure and adult microbiota diversity are discussed. When possible, we draw connections between observed changes to relative bacterial abundance, proposed bacterial functions, and downstream effects to brain development. We also explore how the gut microbiome might modify the toxicity of lead by impeding the uptake of lead across the gastrointestinal tract or through indirect mechanisms in such ways that the gut microbiome does not fit within a mediating pathway. In this case, promoting the buffering capacity of the gut microbiome may reduce the impacts of lead on child neurodevelopment. The goal of this short review is to bring attention to the potential role of the gut microbiome in the associations between lead exposure and child neurodevelopment with an eye towards intervention.

Ocular surface microbiome: Influences of physiological, environmental, and lifestyle factors

PURPOSE

The ocular surface (OS) microbiome is influenced by various factors and impacts on ocular health. Understanding its composition and dynamics is crucial for developing targeted interventions for ocular diseases. This study aims to identify host variables, including physiological, environmental, and lifestyle (PEL) factors, that influence the ocular microbiome composition and establish valid associations between the ocular microbiome and health outcomes.

METHODS

The 16S rRNA gene sequencing was performed on OS samples collected from 135 healthy individuals using eSwab. DNA was extracted, libraries prepared, and PCR products purified and analyzed. PEL confounding factors were identified, and a cross-validation strategy using various bioinformatics methods including Machine learning was used to identify features that classify microbial profiles.

RESULTS

Nationality, allergy, sport practice, and eyeglasses usage are significant PEL confounding factors influencing the eye microbiome. Alpha-diversity analysis revealed significant differences between Spanish and Italian subjects (p-value < 0.001), with a median Shannon index of 1.05 for Spanish subjects and 0.59 for Italian subjects. Additionally, 8 microbial genera were significantly associated with eyeglass usage. Beta-diversity analysis indicated significant differences in microbial community composition based on nationality, age, sport, and eyeglasses usage. Differential abundance analysis identified several microbial genera associated with these PEL factors. The Support Vector Machine (SVM) model for Nationality achieved an accuracy of 100%, with an AUC-ROC score of 1.0, indicating excellent performance in classifying microbial profiles.

CONCLUSION

This study underscores the importance of considering PEL factors when studying the ocular microbiome. Our findings highlight the complex interplay between environmental, lifestyle, and demographic factors in shaping the OS microbiome. Future research should further explore these interactions to develop personalized approaches for managing ocular health.

Longitudinal phage-bacteria dynamics in the early life gut microbiome

Microbial colonization of the human gut occurs soon after birth, proceeds through well-studied phases and is affected by lifestyle and other factors. Less is known about phage community dynamics during infant gut colonization due to small study sizes, an inability to leverage large databases and a lack of appropriate bioinformatics tools. Here we reanalysed whole microbial community shotgun sequencing data of 12,262 longitudinal samples from 887 children from four countries across four years of life as part of the The Environmental Determinants of Diabetes in the Young (TEDDY) study. We developed an extensive metagenome-assembled genome catalogue using the Marker-MAGu pipeline, which comprised 49,111 phage taxa from existing human microbiome datasets. This was used to identify phage marker genes and their integration into the MetaPhlAn 4 bacterial marker gene database enabled simultaneous assessment of phage and bacterial dynamics. We found that individual children are colonized by hundreds of different phages, which are more transitory than bacteria, accumulating a more diverse phage community over time. Type 1 diabetes correlated with a decreased rate of change in bacterial and viral communities in children aged one and two. The addition of phage data improved the ability of machine learning models to discriminate samples by country. Finally, although phage populations were specific to individuals, we observed trends of phage ecological succession that correlated well with putative host bacteria. This resource improves our understanding of phage-bacteria interactions in the developing early life microbiome.

Topology of gut Microbiota Network and Guild-Based Analysis in Chinese Adults

Gut microbiota with co-abundant behaviors is considered belonging to the same guild in micro-ecosystem. In this study, we established co-abundance networks of operational taxonomic units (OTUs) among 2944 Chinese adults from the Shanghai Men's and Women's Health Studies and observed a positive connection-dominated scale-free network using Sparse Correlations for Compositional data (SparCC). The closeness centrality was negatively correlated with other degree-based topological metrics in the network, indicating the isolated modularization of the bacteria. A total of 130 guilds were constructed, with a high modularity of 0.68, and retaining more diversity of OTUs than genus classification. The scores of guild structure similarity for comparisons between all, the healthy and the unhealthy subjects were higher than those derived from randomized permutations, suggesting a robust guild structure. We further used the constructed 130 guilds as the aggregation units to identify gut microbiota that may be associated with type 2 diabetes, and found that the OTUs in 21 significant guilds relevant to diabetes belonged to 19 of 41 (46.3%) previously reported genera (derived from Disbiome database), while only 10 (24.4%) showed different abundances between diabetes patients and healthy subjects in genus-based analysis. Our study reveals modularization of gut microbiota as guilds in Chinese populations, and demonstrates advantages of guild-based analysis in identifying diabetes-related gut bacteria. The analytical method based on microbial networks should be widely used to deepen our understanding of the role of gut microbiota in human health.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-024-00211-8.

A population-scale analysis of 36 gut microbiome studies reveals universal species signatures for common diseases

The gut microbiome has been implicated in various human diseases, though findings across studies have shown considerable variability. In this study, we reanalyzed 6314 publicly available fecal metagenomes from 36 case-control studies on different diseases to investigate microbial diversity and disease-shared signatures. Using a unified analysis pipeline, we observed reduced microbial diversity in many diseases, while some exhibited increased diversity. Significant alterations in microbial communities were detected across most diseases. A meta-analysis identified 277 disease-associated gut species, including numerous opportunistic pathogens enriched in patients and a depletion of beneficial microbes. A random forest classifier based on these signatures achieved high accuracy in distinguishing diseased individuals from controls (AUC = 0.776) and high-risk patients from controls (AUC = 0.825), and it also performed well in external cohorts. These results offer insights into the gut microbiome's role in common diseases in the Chinese population and will guide personalized disease management strategies.

Towards geospatially-resolved public-health surveillance via wastewater sequencing

Wastewater is a geospatially- and temporally-linked microbial fingerprint of a given population, making it a potentially valuable tool for tracking public health across locales and time. Here, we integrate targeted and bulk RNA sequencing (N = 2238 samples) to track the viral, bacterial, and functional content over geospatially distinct areas within Miami Dade County, USA, from 2020-2022. We used targeted amplicon sequencing to track diverse SARS-CoV-2 variants across space and time, and we found a tight correspondence with positive PCR tests from University students and Miami-Dade hospital patients. Additionally, in bulk metatranscriptomic data, we demonstrate that the bacterial content of different wastewater sampling locations serving small population sizes can be used to detect putative, host-derived microorganisms that themselves have known associations with human health and diet. We also detect multiple enteric pathogens (e.g., Norovirus) and characterize viral diversity across sites. Moreover, we observed an enrichment of antimicrobial resistance genes (ARGs) in hospital wastewater; antibiotic-specific ARGs correlated to total prescriptions of those same antibiotics (e.g Ampicillin, Gentamicin). Overall, this effort lays the groundwork for systematic characterization of wastewater that can potentially influence public health decision-making.

A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies

BACKGROUND

In microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations.

RESULTS

Our simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more than in previous benchmarks. With extensively parametrized simulations, we benchmark the performance of nineteen differential abundance methods and further evaluate the best ones on confounded simulations. Only classic statistical methods (linear models, the Wilcoxon test, t-test), limma, and fastANCOM properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that adjusted differential abundance testing can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications.

CONCLUSIONS

Tight error control is critical for microbiome association studies. The unsatisfactory performance of many differential abundance methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.

Emergent ecological patterns and modelling of gut microbiomes in health and in disease

Recent advancements in next-generation sequencing have revolutionized our understanding of the human microbiome. Despite this progress, challenges persist in comprehending the microbiome's influence on disease, hindered by technical complexities in species classification, abundance estimation, and data compositionality. At the same time, the existence of macroecological laws describing the variation and diversity in microbial communities irrespective of their environment has been recently proposed using 16s data and explained by a simple phenomenological model of population dynamics. We here investigate the relationship between dysbiosis, i.e. in unhealthy individuals there are deviations from the "regular" composition of the gut microbial community, and the existence of macro-ecological emergent law in microbial communities. We first quantitatively reconstruct these patterns at the species level using shotgun data, and addressing the consequences of sampling effects and statistical errors on ecological patterns. We then ask if such patterns can discriminate between healthy and unhealthy cohorts. Concomitantly, we evaluate the efficacy of different statistical generative models, which incorporate sampling and population dynamics, to describe such patterns and distinguish which are expected by chance, versus those that are potentially informative about disease states or other biological drivers. A critical aspect of our analysis is understanding the relationship between model parameters, which have clear ecological interpretations, and the state of the gut microbiome, thereby enabling the generation of synthetic compositional data that distinctively represent healthy and unhealthy individuals. Our approach, grounded in theoretical ecology and statistical physics, allows for a robust comparison of these models with empirical data, enhancing our understanding of the strengths and limitations of simple microbial models of population dynamics.

Emergent Functional Organization of Gut Microbiomes in Health and Diseases

Continuous and significant progress in sequencing technologies and bioinformatics pipelines has revolutionized our comprehension of microbial communities, especially for human microbiomes. However, most studies have focused on studying the taxonomic composition of the microbiomes and we are still not able to characterize dysbiosis and unveil the underlying ecological consequences. This study explores the emergent organization of functional abundances and correlations of gut microbiomes in health and disease. Leveraging metagenomic sequences, taxonomic and functional tables are constructed, enabling comparative analysis. First, we show that emergent taxonomic and functional patterns are not useful to characterize dysbiosis. Then, through differential abundance analyses applied to functions, we reveal distinct functional compositions in healthy versus unhealthy microbiomes. In addition, we inquire into the functional correlation structure, revealing significant differences between the healthy and unhealthy groups, which may significantly contribute to understanding dysbiosis. Our study demonstrates that scrutinizing the functional organization in the microbiome provides novel insights into the underlying state of the microbiome. The shared data structure underlying the functional and taxonomic compositions allows for a comprehensive macroecological examination. Our findings not only shed light on dysbiosis, but also underscore the importance of studying functional interrelationships for a nuanced understanding of the dynamics of the microbial community. This research proposes a novel approach, bridging the gap between microbial ecology and functional analyses, promising a deeper understanding of the intricate world of the gut microbiota and its implications for human health.

Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis

Humans are colonized with commensal bacteria soon after birth, and, while this colonization is affected by lifestyle and other factors, bacterial colonization proceeds through well-studied phases. However, less is known about phage communities in early human development due to small study sizes, inability to leverage large databases, and lack of appropriate bioinformatics tools. In this study, whole genome shotgun sequencing data from the TEDDY study, composed of 12,262 longitudinal samples from 887 children in 4 countries, is reanalyzed to assess phage and bacterial dynamics simultaneously. Reads from these samples were mapped to marker genes from both bacteria and a new database of tens of thousands of phage taxa from human microbiomes. We uncover that each child is colonized by hundreds of different phages during the early years, and phages are more transitory than bacteria. Participants' samples continually harbor new phage species over time whereas the diversification of bacterial species begins to saturate. Phage data improves the ability for machine learning models to discriminate samples by country. Finally, while phage populations were individual-specific, striking patterns arose from the larger dataset, showing clear trends of ecological succession amongst phages, which correlated well with putative host bacteria. Improved understanding of phage-bacterial relationships may reveal new means by which to shape and modulate the microbiome and its constituents to improve health and reduce disease, particularly in vulnerable populations where antibiotic use and/or other more drastic measures may not be advised.

Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth

BACKGROUND

High-throughput sequencing measurements of the vaginal microbiome have yielded intriguing potential relationships between the vaginal microbiome and preterm birth (PTB; live birth prior to 37 weeks of gestation). However, results across studies have been inconsistent.

RESULTS

Here, we perform an integrated analysis of previously published datasets from 12 cohorts of pregnant women whose vaginal microbiomes were measured by 16S rRNA gene sequencing. Of 2039 women included in our analysis, 586 went on to deliver prematurely. Substantial variation between these datasets existed in their definition of preterm birth, characteristics of the study populations, and sequencing methodology. Nevertheless, a small group of taxa comprised a vast majority of the measured microbiome in all cohorts. We trained machine learning (ML) models to predict PTB from the composition of the vaginal microbiome, finding low to modest predictive accuracy (0.28-0.79). Predictive accuracy was typically lower when ML models trained in one dataset predicted PTB in another dataset. Earlier preterm birth (< 32 weeks, < 34 weeks) was more predictable from the vaginal microbiome than late preterm birth (34-37 weeks), both within and across datasets. Integrated differential abundance analysis revealed a highly significant negative association between L. crispatus and PTB that was consistent across almost all studies. The presence of the majority (18 out of 25) of genera was associated with a higher risk of PTB, with L. iners, Prevotella, and Gardnerella showing particularly consistent and significant associations. Some example discrepancies between studies could be attributed to specific methodological differences but not most study-to-study variations in the relationship between the vaginal microbiome and preterm birth.

CONCLUSIONS

We believe future studies of the vaginal microbiome and PTB will benefit from a focus on earlier preterm births and improved reporting of specific patient metadata shown to influence the vaginal microbiome and/or birth outcomes.

[The microbiome and metabolic syndrome: is this a chicken-or-egg problem?]

The microbiome has become recognized as a critical player in the understanding of human physiology and pathophysiology, especially with regard to the metabolic syndrome. While recent findings emphasize the impact of the microbiome on metabolic health, new questions simultaneously arise: Is there a dysbiotic microbiome before the onset of metabolic disorders or is dysbiosis caused by a deranged metabolism? Furthermore, are there opportunities to employ the microbiome as a tool for novel treatment strategies in patients with metabolic syndrome? The intention of this review article is to describe the fashionable term "microbiome" beyond its current research approaches, which will be relevant to the practicing internist.

Surrogate fostering of mice prevents prenatal estradiol-induced insulin resistance via modulation of the microbiota-gut-brain axis

Introduction

Prenatal and early postnatal development are known to influence future health. We previously reported that prenatal high estradiol (HE) exposure induces insulin resistance in male mice by disrupting hypothalamus development. Because a foster dam can modify a pup's gut microbiota and affect its health later in life, we explored whether surrogate fostering could also influence glucose metabolism in HE offspring and examined mechanisms that might be involved.

Methods

We performed a surrogate fostering experiment in mice and examined the relationship between the metabolic markers associated to insulin resistance and the composition of the gut microbiota.

Results

HE pups raised by HE foster dams (HE-HE) developed insulin resistance, but HE pups fostered by negative control dams (NC-HE) did not. The gut microbiota composition of HE-HE mice differed from that of NC mice raised by NC foster dams (NC-NC), whereas the composition in NC-HE mice was similar to that of NC-NC mice. Compared with NC-NC mice, HE-HE mice had decreased levels of fecal short-chain fatty acids and serum intestinal hormones, increased food intake, and increased hypothalamic neuropeptide Y expression. In contrast, none of these indices differed between NC-HE and NC-NC mice. Spearman correlation analysis revealed a significant correlation between the altered gut microbiota composition and the insulin resistance-related metabolic indicators, indicating involvement of the microbiota-gut-brain axis.

Discussion

Our findings suggest that alterations in the early growth environment may prevent fetal-programmed glucose metabolic disorder via modulation of the microbiota-gut-brain axis. These findings offer direction for development of translational solutions for adult diseases associated with aberrant microbial communities in early life.

Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering

In recent years, unsupervised analysis of microbiome data, such as microbial network analysis and clustering, has increased in popularity. Many new statistical and computational methods have been proposed for these tasks. This multiplicity of analysis strategies poses a challenge for researchers, who are often unsure which method(s) to use and might be tempted to try different methods on their dataset to look for the "best" ones. However, if only the best results are selectively reported, this may cause over-optimism: the "best" method is overly fitted to the specific dataset, and the results might be non-replicable on validation data. Such effects will ultimately hinder research progress. Yet so far, these topics have been given little attention in the context of unsupervised microbiome analysis. In our illustrative study, we aim to quantify over-optimism effects in this context. We model the approach of a hypothetical microbiome researcher who undertakes four unsupervised research tasks: clustering of bacterial genera, hub detection in microbial networks, differential microbial network analysis, and clustering of samples. While these tasks are unsupervised, the researcher might still have certain expectations as to what constitutes interesting results. We translate these expectations into concrete evaluation criteria that the hypothetical researcher might want to optimize. We then randomly split an exemplary dataset from the American Gut Project into discovery and validation sets multiple times. For each research task, multiple method combinations (e.g., methods for data normalization, network generation, and/or clustering) are tried on the discovery data, and the combination that yields the best result according to the evaluation criterion is chosen. While the hypothetical researcher might only report this result, we also apply the "best" method combination to the validation dataset. The results are then compared between discovery and validation data. In all four research tasks, there are notable over-optimism effects; the results on the validation data set are worse compared to the discovery data, averaged over multiple random splits into discovery/validation data. Our study thus highlights the importance of validation and replication in microbiome analysis to obtain reliable results and demonstrates that the issue of over-optimism goes beyond the context of statistical testing and fishing for significance.

Performance of Gut Microbiome as an Independent Diagnostic Tool for 20 Diseases: Cross-Cohort Validation of Machine-Learning Classifiers

Cross-cohort validation is essential for gut-microbiome-based disease stratification but was only performed for limited diseases. Here, we systematically evaluated the cross-cohort performance of gut microbiome-based machine-learning classifiers for 20 diseases. Using single-cohort classifiers, we obtained high predictive accuracies in intra-cohort validation (~0.77 AUC), but low accuracies in cross-cohort validation, except the intestinal diseases (~0.73 AUC). We then built combined-cohort classifiers trained on samples combined from multiple cohorts to improve the validation of non-intestinal diseases, and estimated the required sample size to achieve validation accuracies of >0.7. In addition, we observed higher validation performance for classifiers using metagenomic data than 16S amplicon data in intestinal diseases. We further quantified the cross-cohort marker consistency using a Marker Similarity Index and observed similar trends. Together, our results supported the gut microbiome as an independent diagnostic tool for intestinal diseases and revealed strategies to improve cross-cohort performance based on identified determinants of consistent cross-cohort gut microbiome alterations.

Supervised Machine Learning Enables Geospatial Microbial Provenance

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset-the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85-89% to 90-94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF).

Assessment of Energy and Nutrient Intake and the Intestinal Microbiome (ErNst study): Protocol and Methods of a Cross-sectional Human Observational Study (Preprint)

BACKGROUND

On the national level, nutritional monitoring requires the assessment of reliable representative dietary intake data. To achieve this, standardized tools need to be developed, validated, and kept up-to-date with recent developments in food products and the nutritional behavior of the population. Recently, the human intestinal microbiome has been identified as an essential mediator between nutrition and host health. Despite growing interest in this connection, only a few associations between the microbiome, nutrition, and health have been clearly established. Available studies paint an inconsistent picture, partly due to a lack of standardization.

OBJECTIVE

First, we aim to verify if food consumption, as well as energy and nutrient intake of the German population, can be recorded validly by means of the dietary recall software GloboDiet, which will be applied in the German National Nutrition Monitoring. Second, we aim to obtain high-quality data using standard methods on the microbiome, combined with dietary intake data and additional fecal sample material, and to also assess the functional activity of the microbiome by measuring microbial metabolites.

METHODS

Healthy female and male participants aged between 18 and 79 years were recruited. Anthropometric measurements included body height and weight, BMI, and bioelectrical impedance analysis. For validation of the GloboDiet software, current food consumption was assessed with a 24-hour recall. Nitrogen and potassium concentrations were measured from 24-hour urine collections to enable comparison with the intake of protein and potassium estimated by the GloboDiet software. Physical activity was measured over at least 24 hours using a wearable accelerometer to validate the estimated energy intake. Stool samples were collected in duplicate for a single time point and used for DNA isolation and subsequent amplification and sequencing of the 16S rRNA gene to determine microbiome composition. For the identification of associations between nutrition and the microbiome, the habitual diet was determined using a food frequency questionnaire covering 30 days.

RESULTS

In total, 117 participants met the inclusion criteria. The study population was equally distributed between the sexes and 3 age groups (18-39, 40-59, and 60-79 years). Stool samples accompanying habitual diet data (30-day food frequency questionnaire) are available for 106 participants. Current diet data and 24-hour urine samples for the validation of GloboDiet are available for 109 participants, of which 82 cases also include physical activity data.

CONCLUSIONS

We completed the recruitment and sample collection of the ErNst study with a high degree of standardization. Samples and data will be used to validate the GloboDiet software for the German National Nutrition Monitoring and to compare microbiome composition and nutritional patterns.

TRIAL REGISTRATION

German Register of Clinical Studies DRKS00015216; https://drks.de/search/de/trial/DRKS00015216.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/42529.