IDDF2018-ABS-0157 A revisit on personalised critical variance of gut microbiota during the occurrence of type l diabetes

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Abstract

Background

As well-known, the gut microbiota is associated with many human complex diseases. The change of microbiota community is observed in diseased individuals. However, the disorder of gut microbiota during disease occurrence is still unclear, and especially the pre-disease or early-disease signal on individual gut microbiota requires systematical researches.

Methods

Different from conventional studies on the differential average abundance of gut microbiota between normal and diseased samples, we investigate the variance of the abundance of gut microbiota on consecutive samples from a healthy state to pathogen state for each individual person because the change of microbiota abundance variance would be a critical signal of the biological dynamical system. Edge-network analysis (ENA) is our proposed newly computational approach to analyse the network of associations rather than the network of base variables; especially it can consider the change of variance and covariance simultaneously. Thus we used ENA to integratively analyse the metagenomics data of total 15 individuals from three cohorts in public domain, and each person in the cohorts would have multiple faeces samples for more than one year.

Results

Six individuals keep healthy, and six individuals occur seroconversion, and three individuals occur seroconversion along with final Type 1 Diabetes. Focused on the seroconversion, a key stage to T1D, the conventional analysis found some microbiota with changed abundance after the occurrence of seroconversion (figure 1a). Meanwhile, our analysis further show that a lot of microbiota actually have great abundance variance change before seroconversion (figure 1b), which can even be efficient features to classify the healthy and seroconversion individuals with about 80% accuracy (figure 1c). More importantly, this microbiota with critical abundance variance as key species is also associated with T1D clinical antibody or even some new (figure 1d).

Conclusions

Dissimilar to common biomarkers like a clinical antibody, the individual specific signatures, e.g. variance of gut microbiota abundance, would be an alternative approach for personalised pre-disease or early-disease diagnosis.

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