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Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. The relevance of the human oral microbiome to our understanding of human health has grown in recent years as microbiome studies continue to develop. Given the links of the oral cavity with the digestive, respiratory and circulatory systems, the composition of the oral microbiome is relevant beyond just oral health, impacting systemic processes across the body.
However, we still have a very limited understanding about intrinsic and extrinsic factors that shape the composition of the healthy oral microbiome. Here, we followed a citizen-science approach to assess the relative impact on the oral microbiome of selected biological, social, and lifestyle factors in Spanish individuals.
We found that the oral microbiome changes across age, with middle ages showing a more homogeneous composition, and older ages showing more diverse microbiomes with increased representation of typically low abundance taxa. By measuring differences within and between groups of individuals sharing a given parameter, which is the most common type of grandparenting relationship were able to assess the relative impact which is the most common type of grandparenting relationship different factors in driving specific microbial compositions.
Chronic health disorders present in the analyzed population were the most impactful factors, followed by smoking and the presence of yeasts in the oral cavity. Finally, we corroborate findings in the literature that relatives tend to have more similar oral microbiomes, and show for the first time a similar effect for classmates. Multiple intrinsic and extrinsic factors jointly shape the oral microbiome. Comparative analysis of metabarcoding data from food science courses in germany large sample set allows us to disentangle the individual effects.
The oral cavity is inhabited by an abundant and diverse microbial community, the oral microbiome, which has been related to processes relevant for health and disease 1. The mouth is highly vascularized 2and is an entry point to the respiratory and digestive systems. A multitude of factors, both intrinsic e. Increasing our knowledge on how these factors alter the oral microbiome is important for unveiling the specific roles that certain oral microbes play in disease processes, which in turn may pave the way for the development of innovative microbiome-based diagnostic and therapeutic approaches.
Most studies on the oral microbiome have focused on delineating its changes in the context of common oral diseases such as periodontitis, gingivitis, or dental caries 34. In recent years, however, the relationships of the oral microbiome with systemic diseases or chronic disorders have received growing attention. These include, among others, different cancer types 56cardiovascular diseases 78diabetes 9celiac disease 101112Down Syndrome DS 13or cystic fibrosis What is not needed for a causal explanation Thanks to these studies, we are beginning to understand how oral or systemic disorders relate to changes in the composition of the oral microbiome.
However, given the strong focus on disease, we still lack a sufficient understanding of non-disease parameters that shape the healthy oral microbiome. These intrinsic host biology or extrinsic environment, lifestyle factors are pervasive and likely influence not only the overall composition of the oral microbial ecosystem, but also how it will respond in the context of disease, perhaps predisposing one to either relative dysbiosis or resilience.
A relevant intrinsic factor that has been poorly studied in relation to the oral microbiome is age. To our knowledge, there are no studies using high throughput sequencing techniques which focus specifically on the effects of aging on the oral microbiome in a state of relative health and which include a representative spectrum of ages. Recent reviews that have explored aging largely highlight the tendency toward increased periodontitis and dental caries, but they rely primarily on studies using culture-based identification techniques in regards to alterations in particular taxa 1516 Some studies which have compared age groups have some limitations, such as narrow age what are conflict theory or a focus on age only in the context of particular diseases 151819 Thus, further investigation into the connections between age and the which is the most common type of grandparenting relationship microbiome is warranted.
Lifestyle and hygiene are perhaps the most studied extrinsic factors with respect to changes in the oral microbiome 22 Smoking 24252627wearing braces 28293031and the composition of drinking water 2832 are factors that have been shown to drive particular changes in the oral microbiota. Family members have been shown to display more similar microbiome compositions to each other than to non-family members, while there was not a greater similarity amongst monozygotic twins than amongst dizygotic twins 20333435 Bacteria have received most of the attention in microbiome studies, but other organisms like fungi are also important components.
In the oral cavity, species like Candida albicans have been implicated in dental caries 37which is the most common type of grandparenting relationship it can adhere to the biofilms of the bacterial species Streptococcus mutans and both can act to demineralize tooth enamel 38 One study showed two distinct mycotypes clusters of samples based on the fungal compositionwith one being dominated by Candida species, and the other with higher fungal diversity and Malassezia as the main genus This and another study 41 distinguished which is the most common type of grandparenting relationship with bacterial taxa in Candida -dominated versus other samples, though those results do not seem to coincide entirely.
The interactions between bacteria and fungi are an interesting aspect of the oral microbiome that deserves greater attention. Contrary to disease-focused studies, studies on the overall population enabled by citizen-science provide a unique opportunity to infer the effects of commonly present factors. The dataset comprises oral rinse samples taken from locations across Spain, representing a broad and balanced range of ages.
A subset of the samples were from individuals with chronic disorders that are relevant to the physiology of the oral cavity, and all participants filled out a comprehensive survey with questions about lifestyle, diet, and hygiene habits. To assess the impact of aging on the oral microbiome, we compared the microbial profiles of oral rinse samples across ages, using a subsampling strategy that ensures comparable sample sizes see Materials and Methods.
We first tested for changes in the overall microbiome composition across age, including gender and population as fixed effects in such subsamples see Materials and Methods. We further calculated the homogeneity which is the most common type of grandparenting relationship the microbial composition of samples within a given bin.
This homogeneity test first calculates a spatial median for each age bin a sort of hypothetical centroid composition of the samples within a given age bin, derived from an Aitchison distance matrixthen calculates the distance of each sample in that bin to the spatial median. The n in both plots indicates the number of samples in a given age bin in each subsample.
B Boxes for the distances to the spatial median represent those distances of each sample from the spatial median of its particular age bin, as calculated by the betadisper function. The spatial medians for age bins and the associated ANOVAs were run separately for each of the subsamples, but the boxes here display all such distances for each age bin.
The four respective scatter plots here display only the values from one of those subsamples to give a representative depiction of the trend the same what to put on your tinder profile girl is used for all fourwith age in years along the x -axis. G Genera that increase with age tend to be found at lower abundances while those that decrease with age tend to have greater abundances.
Boxes display the distributions of abundances of genera samples were divided into two age groups merely to generalize the tendencies across age: 13—60 years old, or older than When comparing each age bin separately against the group of all others, for the weighted UniFrac distance, the youngest bin 13—20 was the only bin with a significant result on average across the subsamples Supplementary Fig.
In Table 1the p -values from ANOVA tests for both quadratic and linear models for these alpha diversity values are displayed, showing that indeed the quadratic model better explains the trends across age. We next investigated which organisms show significant differences across age. Our results Table 1 show a number of taxa that increase with age, including the genera AnaeroglobusEikenellaFretibacteriumComamonasOlsenellaand Phocaeicolaas which is the most common type of grandparenting relationship as the phylum Synergistetes, or decrease with age, including the genera AlloprevotellaStreptobacillusHaemophilusPrevotellaGranulicatellaand What is a proximate cause in legal termsas well as the phyla Bacteroidetes and Proteobacteria.
Of note, genera which is the most common type of grandparenting relationship increase with age are typically found at low abundance among all samples, whereas those that decrease with age tend to display the opposite trend Fig. We collected a comprehensive questionnaire regarding over 80 aspects of lifestyle, diet, hygiene, and health from all of the participants in this study.
To assess which of the considered variables had the largest effects on the overall composition of the oral microbiome, we used a PERMANOVA test for each variable with an Aitchison distance matrix, including age, gender, and population as fixed effects see Materials and Methods. For each of the tested variables, subsamples were taken to match the groups in that variable Yes vs No by geographic location, age, and gender. In these comparisons, we excluded samples from donors with any reported chronic disorders, except when the variable of interest was such a disorder.
Our results Fig. We could corroborate the general magnitudes of these effects using a multinomial test which included each of the variables mentioned here, as well which is the most common type of grandparenting relationship age as a continuous value Supplementary Fig. B Boxes represent the distances of each sample from the spatial median of its group Yes in yellow, No in blueas calculated by the betadisper function.
The spatial medians for groups and the associated ANOVAs were run separately for each of the subsamples, but the boxes here display all such distances for each group. Pairs of boxes in both plots are ordered by the absolute value of the difference between the pairs. The n in both plots indicates the number of samples for which a given variable was indicated the same number of matched controls were selected for each subsample test.
A test of homogeneity of each variable showed significant differences when compared to the respective matched controls for CF, DS, the presence of yeast, smoking, and celiac disease Fig. As described above with age groups, the result of this test indicates that the samples of one group for a given variable e. Interestingly, those samples in which yeasts were not detected were more homogeneous than those in which yeasts were detected.
Meanwhile the individuals with CF, DS, and celiac disease, as well as smokers were more homogeneous than those without these disorders and non-smokers, respectively. There was no difference in homogeneity based on hypertension, the use of antibiotics, or the presence of Candidathough as with the general detection of yeast, which is the most common type of grandparenting relationship absence of Candida did tend to present greater homogeneity.
We included only Candida specifically here because it makes up the majority of the yeasts that were detected present in of samples in which yeast was detected and no other genus of yeast appeared in more than eight samples. Some of these variables displayed particular significant differences when compared to their matched controls Table 2. CF 14 and DS 13 have been explored in detail elsewhere, and so are not included which is the most common type of grandparenting relationship this table.
Smokers had higher abundances of MegasphaeraFretibacteriumand Streptococcusand lower abundances of FusobacteriumCapnocytophaga which is the most common type of grandparenting relationship, BergeyellaPorphyromonasLeptotrichiaHaemophilusNeisseriaLautropiaand which is the most common type of grandparenting relationship unclassified genus of the class Gracilibacteria, and also had lower Simpson and Shannon alpha diversity values.
Samples in which yeast were detected, in particular those with Candidahad higher abundance of Lactobacillus. There were no individual taxa that differed significantly for hypertension or antibiotics. Co-occurrence networks represent patterns of which is the most common type of grandparenting relationship that present correlated abundances across different samples We constructed such networks for groups of samples differing in the studied variables and compared them in the search of unique associations between taxa.
From these network comparisons, we derived a score that indicates the relative network uniqueness i. The most unique co-occurrence networks were seen in samples with CF the specifics of this network were discussed in a previous publication 14 and hypertension, followed by the absence of yeast and specifically the absence of Candidathen the other two chronic disorders, DS and celiac, and finally smoking, and the reported use of antibiotics.
Neither did the network uniquenesses show the same trend as the homogeneity results, as hypertension, for instance, showed no difference in homogeneity, yet had which is the most common type of grandparenting relationship second most unique network, while smoking showed one of the strongest differences in homogeneity, and was the second least unique network. We performed predictions of the functional content of our oral microbiome samples in different contexts. Smoking had not only the most KOs that differed significantlybut also the strongest differences, as seen in that heatmap.
DS had a total of 99 significantly different KOs. Those KOs that differed with smoking and those 99 that differed with DS were associated with and 88 pathways, respectively. However, there were many instances of pathways that were associated with some KOs that were increased in smokers and others that were decreased in smokers, and the same for DS Supplementary Fig.
Nonetheless, we used a text mining approach to search for articles that found links between a given pathway and either smoking or DS, also shown in Supplementary Fig. The most prominent pathways found to be significant here and that are associated with smoking in the literature were carbon metabolism articlesfatty acid metabolismexample of cause and effect diagram metabolismbase excision repairand biosynthesis of amino acids For DS, the most prominent were also biosynthesis of amino acids articlescarbon metabolism 28and oxidative phosphorylation There were also trends, either positive or negative, with a number of particular genera Supplementary Table 1.
Our study included groups of samples that belong to members of the same family, and specified different degrees of relationship, such as parents and children, grandparents and grandchildren, partners, siblings, and twins. In addition, given the active participation of schools in our project, we had several groups of samples from students attending the same school. Using an anosim test analysis of similarities on Aitchison distance matrices, we compared the similarity between the microbiome profiles of members of the same family or classroom, to determine whether the similarity was significantly higher than when compared to samples from different families or classes.
With the exception of grandparents and grandchildren, all other relationships showed significantly greater similarity in oral microbiome compositions than was seen between samples from other families or classes Fig. This similarity was highest for twins, followed by siblings, partners, family members which included all of the non-classmate connectionsparents-children, and classmates.
Although the anosim statistic was higher for twins than for siblings, that merely indicates that the trend was stronger in twins. Boxes show the distributions of Aitchison distance values between samples from the same unit blue or different units red. The y axis labels indicate, for each relationship type, the number of samples for which that relationship occurred in at least one other sample, and the number of different units of two or more samples for which that relationship occurred.
This first edition provided a comprehensive snapshot of the oral microbiome composition in adolescents and how it varied with different lifestyle parameters.