Temporal development of the oral microbiome and prediction of early childhood caries

Human microbiomes are predicted to assemble in a reproducible and ordered manner yet there is limited knowledge on the development of the complex bacterial communities that constitute the oral microbiome. The oral microbiome plays major roles in many oral diseases including early childhood caries (ECC), which afflicts up to 70% of children in some countries. Saliva contains oral bacteria that are indicative of the whole oral microbiome and may have the ability to reflect the dysbiosis in supragingival plaque communities that initiates the clinical manifestations of ECC. The aim of this study was to determine the assembly of the oral microbiome during the first four years of life and compare it with the clinical development of ECC. The oral microbiomes of 134 children enrolled in a birth cohort study were determined at six ages between two months and four years-of-age and their mother’s oral microbiome was determined at a single time point. We identified and quantified 356 operational taxonomic units (OTUs) of bacteria in saliva by sequencing the V4 region of the bacterial 16S RNA genes. Bacterial alpha diversity increased from a mean of 31 OTUs in the saliva of infants at 1.9 months-of-age to 84 OTUs at 39 months-of-age. The oral microbiome showed a distinct shift in composition as the children matured. The microbiome data were compared with the clinical development of ECC in the cohort at 39, 48, and 60 months-of-age as determined by ICDAS-II assessment. Streptococcus mutans was the most discriminatory oral bacterial species between health and current disease, with an increased abundance in disease. Overall our study demonstrates an ordered temporal development of the oral microbiome, describes a limited core oral microbiome and indicates that saliva testing of infants may help predict ECC risk

Modèles de mélange dynamiques et suivi longitudinal pour l'inférence de données mixtes et spatio-temporelles : application en Santé Publique

In this thesis, we focus on statistical learning methods for spatio-temporal and mixed-type data. With the rapid growth of public health information systems, a wide range of real-time data is now available for many diseases. The aim is to develop methods for using this data to build operational decision support systems.We first propose a spatio-temporal pipeline for estimating the distribution of a population and highlighting temporal differences. This pipeline is a first step towards a decision support and alert system for the spatio-temporal analysis of population trends. This pipeline is designed so that different distributions and therefore different algorithms can be considered. For an initial application, this pipeline is combined with robust EM algorithms for estimating Gaussian mixture models. It is evaluated using data from Paris hospitals corresponding to people who tested positive for SARS-CoV-2 infection over eleven weeks in 2020.In the second part, we propose a set of algorithms for estimating statistical models on mixed data. We consider that mixed-type data are distributed according to mixtures of laws. We first describe mixture models for various continuous and discrete laws, assuming local independence between discrete and continuous variables. We then propose dynamic algorithms of the EM type, allowing the estimation of all the parameters of the mixture as well as the estimation of the number of classes. We show that our different dynamic algorithms allow us to reach the real number of classes and to correctly estimate the parameters of the discrete and continuous laws. We also highlight the benefits of introducing regularizations to improve performance in situations where the sample size is insufficient for the complexity of the model. These dynamic algorithms are then validated on real data from the literature.Dans cette thèse, nous nous intéressons à des méthodes d'apprentissage statistique sur données spatio-temporelles et données mixtes. En effet, la croissance rapide des systèmes d'informations en santé publique permet aujourd'hui de disposer de données variées en temps réel pour de nombreuses maladies. L'objectif est de développer des méthodes pour utiliser ces données afin de construire des systèmes d'aide à la décision exploitables.Nous proposons d'abord un pipeline spatio-temporel pour estimer la distribution de la population et mettre en évidence des différences temporelles. Ce pipeline est une première étape vers un système d'aide à la décision et d'alerte pour l'analyse spatio-temporelle de l'évolution d'une population. Ce pipeline est conçu de manière à ce que différentes distributions et donc différents algorithmes puissent être envisagés. Pour une première application, ce pipeline est combiné avec des algorithmes EM robustes permettant l'estimation de modèles de mélange gaussiens. Il est éprouvé sur des données d'hôpitaux parisiens correspondant aux personnes testées positives à l'infection par le SARS-CoV-2 sur onze semaines en 2020.Dans une deuxième partie nous proposons un ensemble d'algorithmes pour l'estimation de modèles de mélange sur données mixtes. Nous décrivons d'abord des modèles de mélange pour diverses lois continues et discrètes, en supposant une indépendance conditionnelle entre les variables discrètes et continues. Nous proposons ensuite des algorithmes dynamiques de type EM, permettant l'estimation de tous les paramètres du mélange ainsi que l'estimation du nombre de classes. Nous montrons que nos différents algorithmes dynamiques permettent d'atteindre le nombre réel de classes et d'estimer correctement les paramètres des lois discrètes comme continues. Nous soulignons aussi l'intérêt d'introduire des régularisations sur des paramètres particuliers afin d'améliorer les performances dans des situations où la taille de l'échantillon n'est pas suffisante en regard de la complexité du modèle. Ces algorithmes dynamiques sont ensuite validés sur des données réelles issues de la littérature

Dynamic Expectation-Maximization algorithms for Mixed-type Data

Modelling mixed-type data is still complex because of the heterogeneity of encountered data. With clustering as the objective, many methods are already doing well, but the inference of models and a posteriori exploitation is made difficult if not impossible. In this article we propose methodological developments of mixture models designed for mixed-type data. Component distributions of the continuous attributes can be either Gaussian, Student or Shifted Asymmetric Laplace. Categorical or discrete attributes, assumed independent conditionally on the class membership, can be distributed according to Bernoulli, Multinomial or Poisson distributions. The joint estimation of the number of classes and the parameters is carried out by EM-like algorithms that we have adapted to perform correctly. We show that our different dynamic algorithms allow us to reach the real number of classes and to correctly estimate the parameters of the discrete and continuous laws. We also highlight the benefits of introducing regularization to improve performance in situations where the sample size is insufficient for the complexity of the model. Our various models are then tested on real datasets from the literature, assessing that the objective of jointly estimating the number of components and the model parameters has been achieved

Spatio-temporal mixture process estimation to detect population dynamical changes

Population monitoring is a challenge in many areas such as public health or ecology. We propose a method to model and monitor population distributions over space and time, in order to build an alert system for spatio-temporal data evolution. Assuming that mixture models can correctly model populations, we propose new versions of the Expectation-Maximization algorithm to better estimate both the number of clusters together with their parameters. We then combine these algorithms with a temporal statistical model, allowing to detect dynamical changes in population distributions, and name it a spatio-temporal mixture process (STMP). We test STMP on synthetic data, and consider several different behaviors of the distributions, to adjust this process. Finally, we validate STMP on a real data set of positive diagnosed patients to corona virus disease 2019. We show that our pipeline correctly models evolving real data and detects epidemic changes

Dietary intake influences gut microbiota development of healthy Australian children from the age of one to two years

Early life nutrition is a vital determinant of an individual's life-long health and also directly influences the ecological and functional development of the gut microbiota. However, there are limited longitudinal studies examining the effect of diet on the gut microbiota development in early childhood. Here, up to seven stool samples were collected from each of 48 healthy children during their second year of life, and microbiota dynamics were assessed using 16S rRNA gene amplicon sequencing. Children's dietary information was also collected during the same period using a validated food frequency questionnaire designed for this age group, over five time points. We observed significant changes in gut microbiota community, concordant with changes in the children's dietary pattern over the 12-month period. In particular, we found differential effects on specific Firmicutes-affiliated lineages in response to frequent intake of either processed or unprocessed foods. Additionally, the consumption of fortified milk supplemented with a Bifidobacterium probiotic and prebiotics (synbiotics) further increased the presence of Bifidobacterium spp., highlighting the potential use of synbiotics to prolong and sustain changes in these lineages and shaping the gut microbiota community in young children