Type 1 diabetes mellitus (T1DM) is one of the most common pediatric diseases and its incidence is rising in many countries. Recently, it has been shown that metabolites other than glucose play an important role in insulin deficiency and the development of diabetes. The aim of our study was to look for discriminating variation in the concentrations of small-molecule metabolites in the plasma of T1DM children as compared to non-diabetic matched controls using proton nuclear magnetic resonance (1H-NMR)-based metabolomics.A cross-sectional study was set-up to examine the metabolic profile in fasting plasma samples from seven children with poorly controlled T1DM and seven non-diabetic controls aged 8-18 years, and matched for gender, age and BMI-SDS. The obtained plasma 1H-NMR spectra were rationally divided into 110 integration regions, representing the metabolic phenotype. These integration regions reflect the relative metabolite concentrations and were used as statistical variables to construct (train) a classification model in discriminating between T1DM patients and controls.The total amount of variation explained by the model between the groups is 81.0% [R2Y(cum)] and within the groups is 75.8% [R2X(cum)]. The predictive ability of the model [Q2(cum)] obtained by cross-validation is 50.7%, indicating that the discrimination between the groups on the basis of the metabolic phenotype is valid. Besides the expected higher concentration of glucose, the relative concentrations of lipids (triglycerides, phospholipids and cholinated phospholipids) are clearly lower in the plasma of T1DM patients as compared to controls. Also the concentrations of the amino acids serine, tryptophan and cysteine are slightly decreased.The present study demonstrates that metabolic profiling of plasma by 1H-NMR spectroscopy allows to discriminate between T1DM patients and controls. The metabolites that significantly differ between both groups might point to disturbances in biochemical pathways including (1) choline deficiency, (2) increased gluconeogenesis, and (3) glomerular hyperfiltration. Although the sample size of this study is still somewhat limited and a validation should be performed, the proof of principle looks promising and justifies a deeper investigation of the diagnostic possibilities of 1H-NMR metabolomics in follow-up studies. Trial registration NCT03014908. Registered 06/01/2017. Retrospectively registered.
Next-generation sequencing (NGS) has instigated the research on the role of the microbiome in health and disease. The compositional nature of such microbiome datasets makes it however challenging to identify those microbial taxa that are truly associated with an intervention or health outcome. Quantitative microbiome profiling overcomes the compositional structure of microbiome sequencing data by integrating absolute quantification of microbial abundances into the NGS data. Both cell-based methods (e.g. flow cytometry) and molecular methods (qPCR) have been used to determine the absolute microbial abundances, but to what extend different quantification methods generate similar quantitative microbiome profiles has so far not been explored. Here we compared relative microbiome profiling (without incorporation of microbial quantification) to three variations of quantitative microbiome profiling: 1) microbial cell counting using flow cytometry (QMP); 2) counting of microbial cells using flow cytometry combined with Propidium Monoazide pre-treatment of fecal samples before metagenomics DNA isolation in order to only profile the microbial composition of intact cells (QMP-PMA), and; 3) molecular based quantification of the microbial load using qPCR targeting the 16S rRNA gene. Although qPCR and flow cytometry both resulted in accurate and strongly correlated results when quantifying the bacterial abundance of a mock community of bacterial cells, the two methods resulted in highly divergent quantitative microbial profiles when analyzing the microbial composition of fecal samples from 16 healthy volunteers. These differences could not be attributed to the presence of free extracellular prokaryotic DNA in the fecal samples as sample pre-treatment with Propidium Monoazide did not improve the concordance between qPCR-based and flow cytometry-based QMP. Also lack of precision of qPCR was ruled out as a major cause of the disconcordant findings, since quantification of the fecal microbial load by the highly sensitive digital droplet PCR correlated strongly with qPCR. In conclusion, quantitative microbiome profiling is an elegant approach to bypass the compositional nature of microbiome NGS data, however it is important to realize that technical sources of variability may introduce substantial additional bias depending on the quantification method being used.
Thanks to the 'Limburg Clinical Research Program (LCRP) UHasselt-ZOL-Jessa', supported by the foundation Limburg Sterk Merk, province of Limburg, Flemish government, Hasselt University, Ziekenhuis Oost-Limburg and Jessa Hospital.
The introduction of solid foods is an important dietary event during infancy that causes profound shifts in the gut microbial composition towards a more adult-like state. Infant gut bacterial dynamics, especially in relation to nutritional intake remain understudied. Over 2 weeks surrounding the time of solid food introduction, the day-to-day dynamics in the gut microbiomes of 24 healthy, full-term infants from the Baby, Food & Mi and LucKi-Gut cohort studies were investigated in relation to their dietary intake. Microbial richness (observed species) and diversity (Shannon index) increased over time and were positively associated with dietary diversity. Microbial community structure (Bray-Curtis dissimilarity) was determined predominantly by individual and age (days). The extent of change in community structure in the introductory period was negatively associated with daily dietary diversity. High daily dietary diversity stabilized the gut microbiome. Bifidobacterial taxa were positively associated, while taxa of the genus
To identify the plasma metabolic profile associated with childhood obesity and its metabolic phenotypes.The plasma metabolic profile of 65 obese and 37 normal-weight children was obtained using proton NMR spectroscopy. NMR spectra were rationally divided into 110 integration regions, which reflect relative metabolite concentrations, and were used as statistical variables.Obese children show increased levels of lipids, N-acetyl glycoproteins, and lactate, and decreased levels of several amino acids, α-ketoglutarate, glucose, citrate, and cholinated phospholipids as compared with normal-weight children. Metabolically healthy children show lower levels of lipids and lactate, and higher levels of several amino acids and cholinated phospholipids, as compared with unhealthy children.This study reveals new valuable findings in the field of metabolomics and childhood obesity. Although validation should be performed, the proof of principle looks promising and justifies a deeper investigation of the diagnostic possibilities of proton NMR metabolomics in follow-up studies. Trial registration: NCT03014856. Registered January 9, 2017.
Reliable biomarkers to predict asthma in wheezing preschool children are lacking. Recently, the impact of gut microbial perturbations on the development of asthma gained widespread attention. Gut microbial dysbiosis in the first year of life was associated with asthma in multiple birth cohort studies.1-5 Microbial metabolites might play a crucial role in maintaining an adequate immune balance and preventing asthma through its influence on regulatory T-cells (Tregs) and the Foxp3 gene.1, 6 However, most data are derived from animal studies, whereas most human studies have focused on the association between infant gut microbiota and asthma-like symptoms at an age when a reliable diagnosis of asthma cannot yet be made. Furthermore, no studies have been performed that investigated gut microbial composition in wheezing children, and its association with subsequent development of asthma. In the Asthma DEtection and Monitoring (ADEM) study (clinicaltrial.gov: NCT 00422747), 202 wheezing children and 50 healthy controls aged 2-4 years were prospectively followed until 6 years of age, when a definitive diagnosis of asthma was made. The study was approved by the Dutch national medical ethical committee, and written informed consent was given by all parents. A detailed study protocol was previously published.7, 8 At inclusion, faecal and blood samples were collected. Faecal microbial composition was analysed by sequencing of the 16S rRNA V3-V4 gene region. In total, 230 samples (70 true asthmatics, 114 transient wheezers and 46 healthy controls) were successfully analysed (see flow chart, Figure S1). In blood, atopic sensitisation (Phadiatop Infant test), proportion of Tregs by flow cytometry (CD4+CD25highCD127-), and Foxp3 gene expression were assessed. See Appendix S1 for a detailed description of study methods. The baseline characteristics are displayed in Table S1. First, we examined whether microbial richness and diversity at preschool age were predictive for future asthma development. Neither the microbial richness (OR 0.99 [95%CI 0.98-1.01]; P = .46), nor the microbial diversity as assessed by the Shannon index (OR 1.01 [0.98-1.04]; P = .53) were significantly different between transient wheezing children and true asthmatics while adjusting for potential confounders (sex, breastfeeding, birth season, atopy parents, siblings, parental smoking status, day care attendance; Figure 1A-B). At preschool age, these indices were also not different between wheezers and healthy controls, while adjusting for potential confounders (Figure S2). Next, we examined the overall microbial community structure (as assessed by the Bray-Curtis dissimilarity), which was neither significantly different between transient wheezers and true asthmatics (PERMANOVA P = .07, Figure 1C), nor between preschool wheezers and healthy controls (PERMANOVA P = .22, Figure S2). Microbial profiles of all children were clustered using Dirichlet Multinomial Mixture (DMM) modelling. Three distinct clusters (enterotypes) were identified, that is those that were driven by a relatively high abundance of Bifidobacterium, Bifidobacterium combined with Blautia, and Prevotella combined with Bifidobacterium, respectively. Neither the amount of wheezing children who developed asthma, nor the proportion of children with preschool wheeze were significantly different among the three enterotypes, while adjusting for multiple confounders in multivariable logistic regression analyses (Table S2 and S3). Altogether these results indicate that microbial diversity and overall microbial community structure are not predictive for subsequent asthma development among preschool wheezing children. Furthermore, we examined whether the relative abundance of specific bacterial genera was predictive for future asthma development. Using multivariable logistic regression, we found that the relative abundance of the genera Gemmiger (P = .03) and Escherichia (P = .02) was significantly higher in wheezing children who developed asthma at age 6 years (Figure 2A-B). The risk of developing asthma was highest in those children who harboured the highest relative abundance of these two bacterial genera (Figure 2C-D). In particular, a high relative abundance of Escherichia was associated with 4.6-fold increased odds of asthma (P = .02, Figure 2D). When comparing preschool wheezers with healthy controls, the relative abundance of Collinsella (P = .01) and Dorea (P = .02) was significantly lower in wheezing children (Figure S3). Finally, we examined whether gut microbial profiles were related to atopic sensitisation, Tregs and Foxp3 gene expression. Besides a weak, yet statistically significant, positive correlation between Foxp3 gene expression and bacterial diversity (Shannon index) within the entire study population (Spearman's rho = 0.16; P = .02), atopic sensitisation, Tregs and Foxp3 gene expression were neither associated with the overall microbial community structure (Bray-Curtis dissimilarity) nor with the abundance of specific bacterial genera. To our knowledge, this is the first study to examine the gut microbiota in wheezing preschool children and its association with asthma progression. Our findings suggest that at age 2-4 years, the microbiota perturbations associated with asthma might only be modest. This is in line with the proposed early window-of-opportunity, the first months of life, during which the microbiome is thought to have its strongest impact on immune maturation and tolerance development and stabilises beyond infancy.1, 9 However, this early time-window might not be a suitable age to identify biomarkers for asthma prediction as early asthma symptoms may not have occurred yet. Moreover, the bacterial genera Gemmiger and in particular Escherichia were significantly associated with asthma, suggesting that some microbial dysbiosis might still exist at preschool age among wheezing children prone for developing asthma. In a recent paediatric study, Escherichia was one of three significantly predominant genera in children with asthma compared with healthy controls.10 Furthermore, in a recent adult study, Escherichia was one of two genera that discriminated asthmatics with fixed airway obstruction from those with no airway obstruction.11 These results are in line with our findings, potentially indicating that the abundance of Escherichia in particular may play a role in the early development of asthma. It has been suggested that an increased abundance of aerotolerant bacteria might be a nonspecific response to inflammatory conditions.12 Recently, an increase in Escherichia appeared to reduce butyrate production, which was associated with asthma.10 Potentially, Escherichia abundance has a wider influence on short-chain fatty acids production, thereby causing an immunological dysbalance leading to a Th2-response. The strength of our study is that we assessed the gut microbial profiles of a large group of preschool wheezing children, using modern sequencing techniques. Another strength is a reliable asthma diagnosis at age 6 years based on symptoms, medication use and lung function measurements.8 The current study has several limitations. First of all, microbiome data are high dimensional, and it cannot be excluded that the observed associations are the result of multiple comparisons. Replication in future studies is therefore needed. Additionally, albeit being the first study to examine the gut microbiota in wheezing preschool children and its association with asthma progression, the sample size limits the power of stratified analyses. The number of sensitised preschool children might for example have been too low to detect significant associations when stratifying for atopic and nonatopic asthma. It is also plausible that microbial perturbations especially impact the risk of asthma in children with certain genetic asthma risk loci, which requires stratified analyses for genotype. Finally, the number of children with eczema was substantial in our population, which might have been accompanied by dietary adaptations. Unfortunately, we had no additional information on the children's diets or restrictions. In conclusion, gut microbial diversity and overall gut microbial community structure at age 2-4 years were not associated with preschool wheezing or future asthma development at age 6. When compared to microbial perturbations during infancy, microbial perturbations at preschool age appear to be only modestly associated with asthma. On a genus level, some bacterial genera were associated with wheezing (Collinsella and Dorea) or subsequent development of asthma (Gemmiger and Escherichia), suggesting some microbial dysbiosis in children prone for developing asthma. The role of these genera in the development of asthma warrants further investigation. All authors declare that they have no conflicts of interest. MB and N.v.B. drafted the manuscript; N.v.B., LB and JP were responsible for gut microbial analysis; MB, N.v.B., LB, K.v.d.K, QJ, ED and JP contributed to the design of the study, data collection and interpretation of data. All authors read, revised and approved the final manuscript. This study was funded by the Dutch Foundation for Asthma Prevention (project number SAB 2017/006). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
