Composition and temporal stability of the gut microbiota in older persons - PubMed (original) (raw)

Composition and temporal stability of the gut microbiota in older persons

Ian B Jeffery et al. ISME J. 2016 Jan.

Abstract

The composition and function of the human gut microbiota has been linked to health and disease. We previously identified correlations between habitual diet, microbiota composition gradients and health gradients in an unstratified cohort of 178 elderly subjects. To refine our understanding of diet-microbiota associations and differential taxon abundance, we adapted an iterative bi-clustering algorithm (iterative binary bclustering of gene sets (iBBiG)) and applied it to microbiota composition data from 732 faecal samples from 371 ELDERMET cohort subjects, including longitudinal samples. We thus identified distinctive microbiota configurations associated with ageing in both community and long-stay residential care elderly subjects. Mixed-taxa populations were identified that had clinically distinct associations. Microbiota temporal instability was observed in both community-dwelling and long-term care subjects, particularly in those with low initial microbiota diversity. However, the stability of the microbiota of subjects had little impact on the directional change of the microbiota as observed for long-stay subjects who display a gradual shift away from their initial microbiota. This was not observed in community-dwelling subjects. This directional change was associated with duration in long-stay. Changes in these bacterial populations represent the loss of the health-associated and youth-associated microbiota components and gain of an elderly associated microbiota. Interestingly, community-associated microbiota configurations were impacted more by the use of antibiotics than the microbiota of individuals in long-term care, as the community-associated microbiota showed more loss but also more recovery following antibiotic treatment. This improved definition of gut microbiota composition patterns in the elderly will better inform the design of dietary or antibiotic interventions targeting the gut microbiota.

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Figures

Figure 1

Figure 1

Genus-level (inner circle) and Order-level (outer ring) classifications of OTUs belonging to each of the four major modules, and the average proportion of these classifications in faecal microbiota composition profiles clustered to these modules, using the RDP database.

Figure 2

Figure 2

Workflow indicating the definition of four modules from co-clustering of composition profiles and OTUs. OTUs can cluster into multiple modules to form groups (left) from overlap between modules. OTU group names are generated by combinations of the modules to which the OTUs were assigned. Composition profiles are also allowed to cluster to multiple modules, leading to seven groups (right). The numbers of OTUs and of composition profiles that fall into each of the groups are indicated.

Figure 3

Figure 3

Heat plot of the microbiota composition profiles of subjects in the iBBiG groups (top), against OTUs in the iBBiG groups (side). Day hospital-visiting subjects were classified as ‘Community dwelling'. Rehabilitation subjects were classified as ‘Residential care'.

Figure 4

Figure 4

3-Dimensional PCoA on binary data, highlighting iBBiG-defined microbiota composition profile groups.

Figure 5

Figure 5

Health factors associated with iBBiG-defined microbiota profile groups, showing (a) FIM; (b) Mini Mental State Exam (MMSE); (c) IL-6 levels in the blood (logged); (d) calf circumference; in community-dwelling subjects who were not consuming antibiotics within 1 month prior to sample collection. Kruskal–Wallis test was used to determine significant differences between any composition profile groups.

Figure 6

Figure 6

Boxplots showing the absolute Spearman distance from (a) T0 to T3; (b) T3 to T6; (c) overall from T0 to T6; for subjects classified as community-dwelling (CM), long-stay-dwelling (LS) and unstable subjects from the community (uCM) and from long-stay care (uLS). Significant differences between groups were determined by analysis of variance of linear models.

Figure 7

Figure 7

Diversity boxplots showing differences between stable and unstable community and long-stay subjects at T0. Diversity measures include (a) Shannon; (b) Simpson; (c) Chao1; and (d) Phylogenetic. Significance was determined by analysis of variance of linear models for Shannon, Chao1 and Phylogenetic diversities and by Wilcoxon rank-sum test for Simpson diversity.

Figure 8

Figure 8

Scatter plot of the absolute Spearman distance between T0 and T3 (x axis), against the initial T0 Shannon diversity, highlighting the different iBBiG microbiota composition profile groups, as classified at T0.

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