 Hello. I'd first like to thank Lita for inviting me to talk, and I'd like to thank all the organisers for giving me this opportunity. So, diet microbiota interactions and the elderly. So, why look at microbiota? David Reilman gave you these figures already, but these are good figures, so I don't think there's any harm going through them again. Ten times the number of cells in our body are bacteria to human, and these contain 150 times the number of bacterial genes to human genes. So, this may not be a one-to-one ratio because of splice variants and whatever, but this is still a huge number. And if you think of genes as function, then these bacteria perform an awful lot of beneficial functions throughout the adult life. And these populations are relatively stable in the adults and the state perform. They are involved in the absorption of minerals, the utilization of nutrients from our food. They interact with the immunomodulatory effects at the production of substrates. Even the regulation of insulin sensitivity and appetite control through the free fatty acid receptors 2 and 3. And so, variations in these populations have been associated to a number of disorders including irritable bowel syndrome, IBD, ulcerofglytus, and obesity. So, why look at the gut microbiota in the elderly? Well, first of all, the elderly are an increasing proportion of our society. This is a wonderful success of modern medicine, but it comes with its own challenges. This cohort of our society have an increased susceptibility to infection. And so, C difficile infection is a big problem in residential care. And they also have this sort of systematic increased inflammatory status we refer to as inflammatory aging. But one of the best reasons for looking at the microbiota in the elderly is because there are changes in the microbiota composition and activity in this elderly population. It remains stable for the other life and then as people get older, it begins to change. And so, last but not least, there's the prospect of dietary modulation of these organisms to improve health of this cohort of people. So, to look at this, we need a dataset. And the dataset we use consists of 178 elderly individuals and 13 young controls. The largest group in this dataset are the community-dwelling individuals. But we also have 75 people in residential care. We divided these into two groups, individuals who had been in residential care for longer than six weeks. We refer to these as long stay and less than six weeks and we refer to these as rehabilitation. Within the people who live in community, we have 20-day hospital individuals who attend outpatient care. And for these individuals, we have a number of datasets. The primary dataset is a 16S ribosomal DNA amplicon dataset that measures the taxonomic composition for the individual subjects. But we also have a shotgun dataset based on Illumina sequencing, a metabolomics dataset, and we have dietary data for 168 of our 178 elderly individuals. So, I'm buying partition. When I get a dataset, I want to visualise it. So, we have 5.4 million 16S reeds. We clustered these into 47.5 OTUs. My favourite definition of an OTU is an operational taxonomic unit. It's my favourite definition because it tells you absolutely nothing at all. So, OTUs are just reeds clustered by similarity. In this case, 3%. And they generally correspond to either genera species or genus level taxa. And so, we can see the OTU composition for each individual person. And using this information, we can generate unifract distances and visualise the dataset. So, this is a multivariate plot of the dataset. So, the green samples here are our community samples, and the red up here are our long-stay individuals. And as you can see, there's quite a good separation between the people living in the community and the people living in long-stay based on their microbial composition. The young controls here in purple cluster with the community. And we have individuals in day hospital and rehab who sort of form these intermediate groups. We can visualise this data a second way using one of these heat plots. Just generally, red is there, blue is absent. Along the top here, we have the samples coloured the same as before. Red being the long-stay individuals and green being the community. And as you can see, based on this hierarchical tree, this is the strongest split in the dataset. Along the side here, we have the OTUs. These are clustered. They are also colour-coded at the family and family level. The family level colour coding is blue for battle royalties, brown for firm acutes. And so, what we can see is a couple of things. First of all, a number of taxa are increased in the long-stay individuals. And we also see a number of taxes that are lost in the long-stay individuals compared to the community samples. We can see here that there are some clusters that have lost more microbial taxa than others. And so, we can further divide this tree based on a particular height into eight groups. These are not distinct groups. They are just overlapping clusters. And we can take this information and overlay it back on the previous multivariate plot. And what we can see is that these eight clusters separate spatially along the plot with these clusters over here being associated with the long-stay and these clusters over here being associated with the community samples. So, we also have food data. This food data comes in form of food frequency questionnaires. So, these measure long-term dietary effects. And we have this information for the majority of our subjects. And it consists of 147 food types that are representative of the Irish diet. And these have previously been validated in previous studies. And we also generated what is known as the healthy food diversity index. And this pretty much does exactly what it says on the tin. It measures how diverse and healthy your diet is. Each individual food is given a health value, 0.268 for fruit and veg and 0.0001 for lard. So, this is multiplied up by the diversity of the diet. And so, if you have a diverse diet of fruit and veg, you get a very high value. If you have a low diversity diet of mainly animal products, you get a very low value. And we can visualize this food frequency data using multivariate analysis. Similar to before, you've got your samples up here at the top. We use correspondence. This has the advantage. You can also visualize the variables. And so, up here at the top, you have the samples. Our community samples are in green and our long-stay samples are in red. Very similar to the microbiota. Very good split. Down here, the foods are color coded. Green is fruit and veg. And as you can see, it's skewed off towards the community. Brown is meat, skewed off towards the long-stay, as is blue, which is high sugar, high fat foods. So, generally, the community eats more fruit and veg, long-stay eats more meat and high fat, high sugar foods. Again, we can visualize this with a heat plot. The reason for doing this is again to look at the diversity. We can see that there's a large number of foods here that are just not eaten in the long-term residential care. And a lot of these are fruit and veg. There are a number of foods that have reduced consumption in residential care. And there are a number of foods that have increased consumption. So, the people in residential care generally have a much less diverse diet than the people in the community. And we can, as we can see the same trends in both datasets, we can use a thing called Procrustes analyses to view both datasets together. And so, all this really does is it takes the two datasets that are both sort of in multi-dimensional space, and it twists them in this multi-dimensional space to get the most covariance between the two datasets. And in this way, we're able to visualize both the diet and the microbiota dataset on the same plot where the diet is at the end with the dotted it and the microbiota is at the far end. And so, what we can see, so if we zoom in on the individuals that live in the community, what we can see is that individuals who have this type of diet have a corresponding type of microbiota and individuals who have this type of diet have a corresponding type of microbiota. This is on-weighted unifrec. This is weighted unifrec. And so, up here, we have more of our Privatella-associated microbiota. Down here, it's our bacteroides-associated microbiota. As people enter residential care, their diet starts to change. And after six weeks, this is six weeks to a year, their diet is recognizably that as a person in residential care. And after about a year, the microbiota follows suit and it is sort of recognizably the microbiota of individuals in residential care. So, the diet changes first. The microbiota then follows over the course of the year. So, the diversity of the microbiota and the diet. So, we, down here, we have phylogeic diversity, which is a measure of the microbial diversity in the dataset. And these are our four diet groups that I forget to mention four diet groups. The four diet groups come from... The four diet groups come from this hierarchical tree, where we, again, split it just into four. And the diet group one can be described as low-fat high-fiber. Diet group two can be described as moderate fat and fiber. Diet group three can be described as moderate fat, low-fiber. And diet group four can be described as high-fat, moderate fiber. And so, across these diet groups, you can see there is a decrease in healthy food diversity from the community down into the residential care. And we can see an associated decrease in the microbial diversity from diet group one down to diet group three. There's a small increase between diet group four and diet group three in the microbial diversity. This isn't significant, but I still think it's quite interesting, because there is a small increase of fiber in diet group four, even though there's no increase in the actual healthy food diversity of this group. And so this is reflected in a small increase in microbial diversity. But overall, there is a very good correlation between microbial diversity and healthy food diversity within this dataset. So a more diverse diet leads to a more diverse microbiota. We also have Metabolomics dataset. So these are two multivariate plots where we separate, we can see there's a separation between community and long stay. And we can end community and rehab. And so how does this relate to the microbiota? So there's a method that is similar to Procrustes analysis called co-inertia analysis. And so this allows us to visualize the samples and the variables at the same time. So if we just look at the top two panels, so the first panel on the left, and these are the samples, same as before, one side is the start of the arrow is metabolites, the end of the arrow is the microbiota. And you can see that the green community samples separate out from the red long stay samples. And this separation is associated with particularly butyrate and acetate, but also proponate, valerate and gluterate. So the short chain does a reduction in the production of short chain fatty acids within the residential care individuals. And this is associated with a number of microbial changes represented here by the genus level. And we can see our bacteroides, ruminococcus, copperococcus, ulcilobacter among other genera that are more associated with the community than they are with the long stay. You have the most associated genera is the pyro bacteroides. So to generate short chain fatty acids, you need the substrate, i fiber, and you also need the microbial functionality. And so is it a question that is just the reduced fiber, at least to reduce short chain fatty acid production, but we also see reduction in market genes for the production of butyrate and acetate and proponate. So for a butyrate and acetate, this reduction is significant. For proponate, it's just a trend, but this makes sense based on the previous plot where the butyrate and acetate were much more strongly associated with the community than proponate. We also have a number of measures of the health of these elderly individuals. We measure, we use FIM and Barthel to measure frailty. We have information on cognitive decline in the mini mental state exam, and we have information on geriatric depression, nutrition and calf circumference, which is a mid-arm circumference, which is a measure of sarcopenia. And we removed possible confounders. We removed individuals who had used, who had been given antibiotics within the previous month. And we also, in our statistical model, we adjusted for age, gender, location and medication. So to correlate these clinical variables with the microbial populations, we correlated them with the two strongest trends in the dataset, as defined by the multivariate analysis. And so we correlate the clinical variables with the trend going this way and the trend going this way. So this trend is from the community to the long-stage type microbiota. And this trend here is more the high diversity microbiota to the lower diversity microbiota. And interestingly, we see IL-6 and IL-8 being associated with these two axes. And this was previously reported by BIAGEE et al in 2010 being associated with centenarians. And we can see the only centenarian in this dataset are 102-year-old up here, the healthiest person in the residential care. And also associated with the first axes, we see a decrease in castor comfort and weight. And along the second axes, we see an increase in the geriatric depression test. When we look at the community samples just on their own, we also see this decrease in the geriatric depression test. And when we look at the long-stage subjects only, what we see is as we go from more community type microbiota, as the individuals lose their community type microbiota, there is an increase in frailty and a decrease in castor comfort and weight and BMI. If we adjust for food, the association with weight and BMI disappears. From the high diversity microbiota to the lower diversity microbiota, we also see that as you move from high diversity to low diversity, there's an increase in frailty and there's also an increase in this inflammatory marker at the C-reactive protein, high levels which is indicative of poor health. So just to give you an idea of what size effects we're dealing with here. So the colours here are the same as the colours here, are the community individuals in the middle, the long-stage at either side, high diversity, low diversity. And you can see that the blue here are the youngest of the long-stage, followed by the cyan, followed by the red, followed by the yellow, where the black is the, the gray is the oldest. And so there's about a five-year difference on average between the red here and the yellow here. And when we look at the frailty, what we can see is that even though these people are younger, they've got a higher level of frailty than these cyan people over here. The level of frailty, the difference, the level of frailty between these is quite similar considering that these individuals are five years older and these people are the most frail. So there's an increase in frailty associated with the low-diversity marker builder. We also see an increase in the C-reactive protein associated with the low-diversity marker builder, particularly compared to the high-diversity marker builder. So these individuals are older than both the blue and the red here, and yet they have a much lower level of CRP. So in summary, the marker builder in the elderly is different depending on community location. This is driven by habitual diet. The marker builder alterations correlate with health, especially in the long stay. And so the hypothesis here is that diet shapes got a marker builder which may impact on health in elderly people. And so we're hoping this leads to carefully designed dietary interventions to promote healthier aging. And this is what we're doing as part of the New Age Consortium. So the New Age Consortium consists of about 30 different partners across Europe and beyond. And we're going to look at 1,250 individuals in five different countries, the UK, the Netherlands, France, Italy and Poland. Half of these individuals will be given a healthy Mediterranean-style diet, while half will be just given their regular diet for 12 months. Over these, we'll have their marker builder information from before the diet and after, and we'll be measuring health indices throughout, as well as epigenetic and metabolomic data sets. So it's going to be a very interesting long-term intervention. My challenges are micro-builder modulation and restoration in this cohort and the challenges in lots of different cohorts. And associated with this is the use of prospective long-achieval study and interventions. I would also like to echo Janet the need for multiple omics data sets to really understand in a sort of integrative comprehensive way what is happening in the gut microbiota and the generation of dietary guidelines informed by the needs of the microbiota as we age. I'd like to thank you all for your attention. I'd be happy to take any questions. Thank you very much. Most important bit. Acknowledging the people I work with, I'd like to acknowledge Paula too and Marcus Clawson. I'd also like to give a mention to Mr Hugh Harris and Dr Eilish O'Connor. Thank you very much. This talk is open for questions. In your big European study, you're not going to take the Italians off their Mediterranean diets and put them on British-studied diets, are you? That would be an interesting study. The diet is going to be formulated specifically for elderly individuals. The needs of the elderly are going to be taken into account. Part of the project will be to formulate an elderly-type food pyramid. Hopefully we'll be able to improve somewhat on the diet that they are already on. Shannon, in yogurt consumption, and whether that was reflected in any changes? The short answer is there wasn't much difference in yogurt consumption, and we have not been able to identify compositional changes in the microbiota-associated consumption of yogurts in this data set. Did you look at differences in antibiotic consumption? Antibiotic individuals were excluded if they had taken antibiotics a month prior. Although the effects of antibiotic treatment go beyond a month, these effects generally reflect the increase in antibiotic-resistant genes, and also maybe a loss of some species, such as Bift of Bacterium species are very prone to antibiotic treatment, and it's known that there's a loss of Bift species in elderly individuals. Well, there's a lowering of the diversity of the Bift species in elderly individuals, with the exception of maybe Biftalocentus. I would argue that after antibiotic treatment, this new stable microbiota is then the microbiota associated with the individuals. It would be impossible to exclude every individual that had antibiotic treatment for a long period of time due to the prevalence of antibiotic use in our society. By the time someone is treated, they've had multiple courses of antibiotic treatment, so it's no different for the elderly individuals. It would be a factor that would destabilise the microbiome, I think, and induce some changes in the elderly. Will please join me in showing appreciation for this speaker. Thanks. That was a very interesting talk, and now I'd like to introduce our next talk, which will be equally interesting and is in the pediatric realm, which is of interest to many of us here. This is Catherine Dewey, and she is from the University of California at Davis, and will be speaking to us on diet, child nutrition and microbiome. Welcome, Dr. Dewey.