Environmental factors shaping the gut microbiome in a Dutch population.

The gut microbiome is associated with diverse diseases1-3, but a universal signature of a healthy or unhealthy microbiome has not been identified, and there is a need to understand how genetics, exposome, lifestyle and diet shape the microbiome in health and disease. Here we profiled bacterial composition, function, antibiotic resistance and virulence factors in the gut microbiomes of 8,208 Dutch individuals from a three-generational cohort comprising 2,756 families. We correlated these to 241 host and environmental factors, including physical and mental health, use of medication, diet, socioeconomic factors and childhood and current exposome. We identify that the microbiome is shaped primarily by the environment and cohabitation. Only around 6.6% of taxa are heritable, whereas the variance of around 48.6% of taxa is significantly explained by cohabitation. By identifying 2,856 associations between the microbiome and health, we find that seemingly unrelated diseases share a common microbiome signature that is independent of comorbidities. Furthermore, we identify 7,519 associations between microbiome features and diet, socioeconomics and early life and current exposome, with numerous early-life and current factors being significantly associated with microbiome function and composition. Overall, this study provides a comprehensive overview of gut microbiome and the underlying impact of heritability and exposures that will facilitate future development of microbiome-targeted therapies.

Structural EEG analysis: an explorative study.

A method is described to detect (subtle) changes in an EEG (electroencephalogram) by means of a Markovian modeling approach. This method, termed structural EEG analysis, treats the non-stationary EEG as a sequence of a finite number of short elementary patterns. Subtle changes in an EEG may be detected by studying the transition probabilities between the different patterns. By viewing the patterns as states in a Markov chain, a representation of the EEG structure based on a state transition probability matrix emerges. Various techniques to estimate the state transition probability matrices have been investigated. A number of experiments were performed with artificially generated data to determine the data length required to obtain a reliable estimate of the transition matrices. It appeared that a data length of approximately five to eight times the number of entries in the matrices is needed to accurately estimate the matrices. It was determined that the data length required to reliably estimate the transition probability matrix is dependent on the number of states and the number of non-zero entries of the matrix. Also, the data length appears independent of the values of the probabilities. The structural analysis approach was applied to actual EEG data, recorded from normal volunteers and epileptic subjects. It was demonstrated that visually confirmable changes in the EEG could be detected by the structural analysis method more accurately than by a more conventional approach.

Classification of sleep patterns by means of markov modeling and correspondence analysis.

Shown is how correspondence analysis can be used to track changes in an individuals' sleep pattern. Correspondence analysis was applied to sleep stage transition matrices computed from all-night sleep of normal, obese, and apnoetic subjects. Differences between the groups, and intraindividual changes in sleep patterns could be visualized better than with a x2-based clustering approach.