Model Selection Reveals the Butyrate-Producing Gut Bacterium Coprococcus eutactus as Predictor for Language Development in 3-Year-Old Rural Ugandan Children.

INTRODUCTION: The metabolic activity of the gut microbiota plays a pivotal role in the gut-brain axis through the effects of bacterial metabolites on brain function and development. In this study we investigated the association of gut microbiota composition with language development of 3-year-old rural Ugandan children. METHODS: We studied the language ability in 139 children of 36 months in our controlled maternal education intervention trial to stimulate children's growth and development. The dataset includes 1170 potential predictors, including anthropometric and cognitive parameters at 24 months, 542 composition parameters of the children's gut microbiota at 24 months and 621 of these parameters at 36 months. We applied a novel computationally efficient version of the all-subsets regression methodology and identified predictors of language ability of 36-months-old children scored according to the Bayley Scales of Infant and Toddler Development (BSID-III). RESULTS: The best three-term model, selected from more than 266 million models, includes the predictors Coprococcus eutactus at 24 months of age, Bifidobacterium at 36 months of age, and language development at 24 months. The top 20 four-term models, selected from more than 77 billion models, consistently include C. eutactus abundance at 24 months, while 14 of these models include the other two predictors as well. Mann-Whitney U tests suggest that the abundance of gut bacteria in language non-impaired children (n = 78) differs from that in language impaired children (n = 61). While anaerobic butyrate-producers, including C. eutactus, Faecalibacterium prausnitzii, Holdemanella biformis, Roseburia hominis are less abundant, facultative anaerobic bacteria, including Granulicatella elegans, Escherichia/Shigella and Campylobacter coli, are more abundant in language impaired children. The overall predominance of oxygen tolerant species in the gut microbiota was slightly higher in the language impaired group than in the non-impaired group (P = 0.09). CONCLUSION: Application of the all-subsets regression methodology to microbiota data established a correlation between the relative abundance of the anaerobic butyrate-producing gut bacterium C. eutactus and language development in Ugandan children. We propose that the gut redox potential and the overall bacterial butyrate-producing capacity in the gut are important factors for language development.

A new randomization procedure based on multiple covariates and applicable to parallel studies with simultaneous enrollment of all subjects prior to intervention.

BACKGROUND: Parallel intervention studies involving volunteers usually require a procedure to allocate the subjects to study-arms. Statistical models to evaluate the different outcomes of the study-arms will include study-arm as a factor along with any covariate that might affect the results. To ensure that the effects of the covariates are confounded to the least possible extent with the effects of the arms, stratified randomization can be applied. However, there is at present no clear-cut procedure when there are multiple covariates. METHODS: For parallel study designs with simultaneous enrollment of all subjects prior to intervention, we propose a D-optimal blocking procedure to allocate subjects with known values of the covariates to the study arms. We prove that the procedure minimizes the variances of the baseline differences between the arms corrected for the covariates. The procedure uses standard statistical software. RESULTS: We demonstrate the potential of the method by an application to a human parallel nutritional intervention trial with three arms and 162 healthy volunteers. The covariates were gender, age, body mass index, an initial composite health score, and a categorical indicator called first-visit group, defining groups of volunteers who visit the clinical centre on the same day (17 groups). Volunteers were allocated equally to the study-arms by the D-optimal blocking procedure. The D-efficiency of the model connecting an outcome with the study-arms and correcting for the covariates equals 99.2%. We simulated 10,000 random allocations of subjects to arms either unstratified or stratified by first-visit group. Intervals covering the middle 95% of the D-efficiencies for these allocations were [82.0, 92.0] and [93.2, 98.4], respectively. CONCLUSIONS: Allocation of volunteers to study-arms with a D-optimal blocking procedure with the values of the covariates as inputs substantially improves the efficiency of the statistical model that connects the response with the study arms and corrects for the covariates. TRIAL REGISTRATION: Dutch Trial Register NL7054 ( NTR7259 ). Registered May 15, 2018.

Factors of concern in a human 3D cellular airway model exposed to aerosols of nanoparticles.

Mucilair 3D bronchial airway models, cultured at an air-liquid interface, were exposed to aerosols of copper oxide (CuO) nanoparticles in Vitrocell air exposure modules. Four cell donors, four exposure modules and four exposure concentrations were varied within four different exposure sessions using a statistical experimental design called a hyper-Graeco-Latin square. Analysis of variance techniques were used to investigate the effects of these factors on release and RNA expression of inflammation markers monocyte chemoattractant protein-1 (MCP-1) interleukines 6 and 8 (IL-6 and IL-8) an cytotoxicity marker lactate dehydrogenase (LDH) determined 24h after exposure. The same techniques were also used to conduct a global analysis on RNA expressions of 10,000 genes. There were no major signs of cytotoxicity. Release of IL-6 and MCP-1 was affected by CuO concentration, and, for MCP-1, by donor variation. IL-8 release was not affected by these factors. However, gene expression of all three inflammation markers was strongly affected by CuO concentration but not by the other factors. Further, among the 10,000 genes involved in the global analysis of RNA expression, 1736 were affected by CuO concentration, 704 by donor variation and 269 by variation among exposure sessions. The statistical design permitted the assessment of the effect of CuO nanoparticles on 3D airway models independently of technical or experimental sources of variation. We recommend using such a design to address all potential sources of variation. This is especially recommended if test materials are expected to be less toxic than CuO, because the variation among the concentration levels could then be close to the variation among donors or exposure sessions.

Controlling false discovery rates in factorial experiments with between-subjects and within-subjects tests.

BACKGROUND: The False Discovery Rate (FDR) controls the expected number of false positives among the positive test results. It is not straightforward how to conduct a FDR controlling procedure in experiments with a factorial structure, while at the same time there are between-subjects and within-subjects factors. This is because there are P-values for different tests in one and the same response along with P-values for the same test and different responses. FINDINGS: We propose a procedure resulting in a single P-value per response, calculated over the tests of all the factorial effects. FDR control can then be based on the set of single P-values. CONCLUSIONS: The proposed procedure is very easy to apply and is recommended for all designs with factors applied at different levels of the randomization, such as cross-over designs with added between-subjects factors. TRIAL REGISTRATION: NCT00959790.