Mediterranean diet intervention alters the gut microbiome in older people reducing frailty and improving health status: the NU-AGE 1-year dietary intervention across five European countries

Abstract

Objective: Ageing is accompanied by deterioration of multiple bodily functions and inflammation, which collectively contribute to frailty. We and others have shown that frailty co-varies with alterations in the gut microbiota in a manner accelerated by consumption of a restricted diversity diet. The Mediterranean diet (MedDiet) is associated with health. In the NU-AGE project, we investigated if a 1-year MedDiet intervention could alter the gut microbiota and reduce frailty.

Design: We profiled the gut microbiota in 612 non-frail or pre-frail subjects across five European countries (UK, France, Netherlands, Italy and Poland) before and after the administration of a 12-month long MedDiet intervention tailored to elderly subjects (NU-AGE diet).

Results: Adherence to the diet was associated with specific microbiome alterations. Taxa enriched by adherence to the diet were positively associated with several markers of lower frailty and improved cognitive function, and negatively associated with inflammatory markers including C-reactive protein and interleukin-17. Analysis of the inferred microbial metabolite profiles indicated that the diet-modulated microbiome change was associated with an increase in short/branch chained fatty acid production and lower production of secondary bile acids, p-cresols, ethanol and carbon dioxide. Microbiome ecosystem network analysis showed that the bacterial taxa that responded positively to the MedDiet intervention occupy keystone interaction positions, whereas frailty-associated taxa are peripheral in the networks.

Conclusion: Collectively, our findings support the feasibility of improving the habitual diet to modulate the gut microbiota which in turn has the potential to promote healthier ageing.

Keywords: ageing; diet; enteric bacterial microflora; inflammation; intestinal bacteria.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1

Baseline habitual diet and microbiota composition separate and co-vary by country, and the dietary intervention altered macronutrient profiles. Principal component analysis (PCoA) plots of (A) baseline dietary profiles and (B) baseline 16S microbiome profiles across the five different countries. For both, the PERMANOVA p values showing the significance of the association with the countries are also indicated. For the association between the dietary frequencies, the microbiome profiles, R2 and the significance values obtained using the Procrustes analysis are also shown. The results indicate that there are country-specific patterns in dietary habits which are also reflected in the microbiome profiles. (C) PCoA plots showing the distinct variations in the dietary patterns in the intervention and control cohorts. The PERMANOVA p values of these differences are also indicated. This reflects the effect of the dietary intervention to detect the specific dietary components driving these effects. Associations were computed between the intake frequencies of the components and the two PCoA axes (PCoA1 and PCoA2). These associations are plotted in (D). While the intervention group is primarily driven by an increase in consumption of fibres, vitamins (C, B6, B9, thiamine) and minerals (Cu, K, Fe, Mn, Mg), the changes in controls are associated with an increase in fats consumption.

Figure 2

Identification of diet responsive taxa by machine learning. (A) Correlation between the actual and predicted diet scores obtained using the random Forest approach. (B) Ranked feature importance scores of the top marker Operational Taxonomic Units (OTUs) responding positively and negatively to diet, along with their taxonomic affiliations (see Methods section for the selection of the top markers significantly associated with the food score). Top markers having a significant positive or negative association with diet scores were tagged as ‘DietPositive’ and ‘DietNegative’, respectively. The two groups show distinct taxonomic classifications. While DietPositive markers have an over-representation of species like Faecalibacterium prausnitzii, Eubacterium and Roseburia, DietNegative markers are characterised by the presence of Ruminococcus torques, Collinsella aerofaciens, Coprococcus comes, Dorea formicigenerans, Clostridium ramosum. The associations of the different groups with the adherence scores are also reflected in the changes across the time points between the intervention and control cohorts (as shown in C). (C) Boxplot showing the log-fold change in the gain/loss ratios of the various taxa (ie, the number of individuals in which a given OTU is increased divided by the number of individuals in which it is decreased across the time points) in the intervention cohorts compared with non-intervention in the two groups. While the DietPositive OTUs had a relatively positive increase in the intervention cohort (compared with the non-intervention group), changes in the DietNegative indicated a significant decrease with the intervention. (D) Boxplots showing the variation in the across time point changes in the DietPositive and the DietNegative OTUs in groups of individuals obtained after dividing them into three tertile groups (low, medium and high) based on increasing positive changes in adherence to the NU-AGE diet. The p values of the significance of the association are indicated as ****p<0.0001, ***p<0.001, **p<0.01 and *p<0.05.

Figure 3

Consistent association of diet responsive taxa with different measures of frailty, cognitive function and inflammation. (A) Heatmap showing the variation of the association patterns (obtained using Spearman rhos) of the adherence associated marker Operational Taxonomic Units (OTUs) (arranged from top to bottom in increasing order of their correlations with the adherence scores) with the selected measures of frailty, cognitive function and the pro/anti-inflammatory cytokine levels. For each cell, colours indicate the Spearman rho values (as shown). **Significant association with FDR-corrected p value

Figure 4

MedDiet microbiome index correlates with reduced frailty, improved cognitive function and reduced inflammation, independent of the adherence scores. Violin plot showing the association (partial Spearman correlations) of the different measures of frailty, cognitive function and inflammatory marker levels with the MedDiet-modulated microbiome index after taking into account the adherence scores as a confounder. The x axis shows the Spearman rho values and the y axis indicates the −log (base 10) of the p values. Most negatively associated measures are expected to be at the extreme left of the plot, the most positively associated measures are expected to be at the extreme right of the plot. Points are coloured based on the significance of the obtained associations (red indicates associations with FDR-corrected p

Figure 5

Bacterial taxa that respond positively to Mediterranean diet intervention occupy keystone interaction nodes for peripheral frailty-associated taxa in microbiome networks. (A) Representation of the Operational Taxonomic Unit (OTU) co-occurrence network obtained for all the samples across the time points and cohorts with the DietPositive, DietNegative and non-correlated OTUs shown in green, red and grey colours, respectively. The network shows two distinct characteristics of the DietPositive and DietNegative markers (or OTUs). While the DietNegative markers (barring a few exceptions) are observed to occur as the peripheral nodes in the network, the DietPositive markers mostly act as either the centrally connected hub nodes or as interconnecting nodes between the hubs, indicating their centrality to the microbiome. This is also reflected in the comparison of the degree and betweenness centrality measures shown as boxplots in (B) and (C), respectively. (D) Relative co-occurrence propensity (calculated as the logged ratio of the number of positive edges to the number of negative edges) between the DietPositive and DietNegative OTUs with those belonging to the different iterative Binary Bi-clustering of Gene-sets (iBBiG) modules. It was observed that, specifically for the frailty-associated longstay-like module C, while the DietNegative markers showed a positive co-occurrence, the DietPositive markers showed a negative association, further indicating that taxa that respond positively to the diet negatively associate with those that are associated with frailty. (E) The negative association was further investigated by building networks for the five overlapping windows of samples W1–W5 (see Methods section), with increasing adherence to the diet. Relative co-occurrence propensity between the DietPositive and the module C across networks obtained for the overlapping windows of samples with increasing adherence to the diet. With increasing adherence to the diet, the relative co-occurrence propensity between the DietPositive OTUs and those belonging to the module C becomes increasingly negative. The p values of the significance of association are indicated as ****p