1. Computational and Systems Biology
  2. Microbiology and Infectious Disease
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Adjusting for age improves identification of gut microbiome alterations in multiple diseases

  1. Tarini S Ghosh
  2. Mrinmoy Das
  3. Ian B Jeffery
  4. Paul W O'Toole  Is a corresponding author
  1. APC Microbiome Ireland, University College Cork, Ireland
  2. School of Microbiology, University College Cork, Ireland
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Cite this article as: eLife 2020;9:e50240 doi: 10.7554/eLife.50240

Abstract

Interaction between disease-microbiome associations and ageing has not been explored in detail. Here, using age/region-matched sub-sets, we analysed the gut microbiome differences across five major diseases in a multi-cohort dataset constituting more than 2500 individuals from 20 to 89 years old. We show that disease-microbiome associations display specific age-centric trends. Ageing-associated microbiome alterations towards a disease-like configuration occur in colorectal cancer patients, thereby masking disease signatures. We identified a microbiome disease response shared across multiple diseases in elderly subjects that is distinct from that in young/middle-aged individuals, but also a novel set of taxa consistently gained in disease across all age groups. A subset of these taxa was associated with increased frailty in subjects from the ELDERMET cohort. The relevant taxa differentially encode specific functions that are known to have disease associations.

eLife digest

The human body is an ecosystem made up of both human cells and trillions of microbes, and the largest microbial community is in the gut. This community of gut microbes helps harvest nutrients from our food, modulates our immune system, and even affects our mood. Infectious and chronic diseases appear to cause changes in the make-up of the gut microbiome, while microbiome changes may increase the risk of some non-infectious diseases. Learning more about these disease-linked changes in the gut microbiome may therefore help scientists to develop new tests and treatments. To do this, scientists need to understand which microbes play a role in individual diseases, if risk-related microbes are gained or helpful microbes lost in patients with particular diseases, and if certain changes in gut microbes occur across many diseases.

Ageing also changes the gut microbes. This may happen because older individuals eat a less complex diet and are likely to take many medications that may alter the microbes in their gut. Because of this, age may affect changes in gut microbes associated with diseases. This highlights the need for studies that tease apart the importance of ageing-related and disease-related changes in the gut microbiome.

Now, Ghosh et al. show that gut microbe changes linked to diseases may vary with a person’s age. The analysis compared the gut microbiomes of more than 2,500 individuals aged 20 to 89. This included individuals with inflammatory bowel disease, colorectal cancer, type 2 diabetes, intestinal polyps and liver cirrhosis. The study revealed that younger people gradually gain disease-associated gut microbes, while older people tend to lose the gut microbes usually found in a healthy gut. Ghosh et al. also identified a set of gut microbes that were gained in many diseases and across age-groups. This set of microbes was also associated with frailty in elderly people. The characteristics of the microbes in this set are all known to have detrimental effects on human health.

This analysis shows how important it is to control for age and other factors that may skew the results of microbiome projects. Future studies are needed to understand why these gut microbe changes occur and what the consequences of these changes are for a person’s health and the course of their disease. This may lead to the development of treatment strategies that help promote a healthy gut microbiome and fight disease throughout life.

Introduction

Alterations in the gut microbiome have been reported for many diseases (Duvallet et al., 2017; Pasolli et al., 2016). Identifying the specificity of these alterations is necessary for developing microbiome-based diagnostic and therapeutic strategies. In addition to investigating the aspect of causality, three key aspects of microbiome-disease alteration need to be clarified (Duvallet et al., 2017; He et al., 2018). The first aspect involves the disease-associated components (key taxa and their metabolic functions) which would help in establishing diagnostics and understanding the mechanisms by which the altered microbiome affects the host. The second aspect concerns the directionality of the associations which is required for designing therapeutic strategies (e.g. targeted antimicrobials versus microbiome restoration). The third aspect is the extent of shared versus disease-specific microbiome alterations that indicate what effect the onset of a given disease may have on the microbiome-related risk of other diseases. Meta-analyses across cases and controls from different geographical locations have identified the components of the microbiome alterations common to multiple diseases, as well as disease-specific alterations (Duvallet et al., 2017; He et al., 2018; Jackson et al., 2018; Pasolli et al., 2016). While certain diseases like colorectal cancer (CRC) are characterized by an increase (or gain) of pathobionts (such as Fusobacterium, Porphyromonas, Parvimonas), the onset of others like Inflammatory Bowel Disease (IBD) is associated with the depletion of specific taxa (e.g. Roseburia, Faecalibacterium) (Duvallet et al., 2017). In contrast, diarrhoeal diseases are accompanied by both an increase of pathobionts (specifically Enterobacteriaceae) as well as lower abundance of commensal taxa. Recent meta-analyses have also shown a surprising degree of overlap of microbiome associations (both at the level of specific taxa as well as specific microbial metabolic pathways) (Armour et al., 2019; Duvallet et al., 2017; Pasolli et al., 2016). Despite these overlaps, links between the microbial taxa and the functional pathways that are consistently altered across multiple diseases have not been explored in detail.

Microbiome-based diagnostics/therapeutics also need to account for several host factors. Variations in region/ethnicity of subjects are linked to the gut microbiome composition and microbiome-based diagnostics in various diseases (Deschasaux et al., 2018; He et al., 2018). In addition to region/ethnicity-specific variations, multiple studies have also shown age to be a strong covariate of microbiome composition (Falony et al., 2016). We, along with others, have previously identified ageing-associated microbiome alterations in the ELDERMET cohort, associated with lower complexity dietary intake and poly-pharmacy (Claesson et al., 2012; O'Toole and Jeffery, 2015; Ticinesi et al., 2017). Besides ageing-associated changes in the microbiome, early and late-onset variants of different diseases are characterized by distinct patho-physiologies (Duricova et al., 2014; Yeo et al., 2017), which could also be linked to age-related microbiome involvement. These observations motivated us to perform an extensive investigation of microbiome-disease alterations across the human age landscape. We aimed to answer the following questions: To what extent does ageing influence microbiome-disease associations for different diseases? Do different diseases show differential microbiome-signatures (both in terms of the microbiome components as well as the directionality of the changes) across age-groups? If so, to what extent do these changes affect the known list of microbiome-based markers for the different diseases? Is there a change in the pattern of multiple disease-associated taxa (as identified by previous meta-analyses) across age groups? And lastly, is it possible to identify a link between these disease markers and the microbial metabolic pathways previously associated with these diseases?

Investigating microbiome alterations in multiple diseases across a wide age-range requires a comprehensive dataset of consistently collated microbiome profiles. The curated MetagenomicData repository of the ExperimentHub R library contains taxonomic and functional pathway profiles of more than 6000 shotgun-sequenced human microbiome samples from different human body sites, spanning more than 30 diseases (and controls) from more than 25 different studies (Pasolli et al., 2017). A further advantage of the curatedMetagenomicData is that these microbiome profiles were created using uniform bioinformatic analysis of the sequenced reads (using metaphlan2 and humann2) (Franzosa et al., 2018; Truong et al., 2015). The repository also provides extensive metadata information for the samples including the study, experimental protocols, region (or country), disease status, age, gender, BMI and antibiotic usage. Although other key factors affecting gut microbiome composition like diet or medication are not provided for all studies, the repository can still facilitate meta-analysis of datasets from multiple studies, to provide initial insights into the effect of other key host associated factors on microbiome disease signatures. Here we analysed the gut microbiome in 15 studies encompassing four diseases, comprising more than 2500 individuals ranging from 20 to 89 years of age, derived from the ExperimentHub repository, the ELDERMET project and recently published studies on IBD) and colorectal cancer CRC (Franzosa et al., 2019; Thomas et al., 2019; Wirbel et al., 2019).

Results

Influence of age on the microbiome and microbiome-disease signatures

We first adopted a stepwise methodology to reduce the confounding effects of DNA sequencing/extraction methodologies on the microbiome profiles from the different studies (See Materials and methods; Figure 1—figure supplement 1), and subsequently retained samples from individuals with age in the range of 20–89 years (excluding cohorts where the ‘controls’ also consisted of hospitalized patients) (Vincent et al., 2016). We supplemented this data set with 475 shotgun metagenome profiles from recently published studies on IBD (Franzosa et al., 2019) and CRC (as ‘Validation’ cohorts) (Thomas et al., 2019; Wirbel et al., 2019), finally assembling a collated set of microbiome taxonomic profiles from more than 2500 samples (including the 189 ELDERMET samples used later in the study) (see Materials and methods; Supplementary file 1 and Supplementary file 2; Figure 1—figure supplement 2).

We next investigated the interaction of metadata with taxonomic profiles. After filtering out redundant and sparse metadata types (recorded for less than 30% of the samples), we performed PERMANOVA linking the effects of each metadata with the gut microbiome accounting for DNA extraction technique as a confounder (See Materials and methods; DNA extraction technique was still treated as a confounder as it still had a minor effect, R2 = 0.019, on the gut microbiome composition). Regional factors namely country and continent had the largest interaction with gut microbiome composition (Figure 1A). Regional factors reflect the ethnicity and other socio-economic properties of the study populations, which has a dominant effect on gut microbiome architecture and microbiome-based disease signatures (Deschasaux et al., 2018; He et al., 2018). Age followed by study-condition (disease versus control status) were the second major effect. We thus explored the variation of the apparently ‘healthy’ microbiome across the age landscape using Principal Component Analysis of 1175 gut microbiome profiles from exclusively ‘control’ individuals from the age-groups’ 20–39’ (categorized as ‘Young’),’ 40–59’ (‘Middle-Age’) and’ above 60’ (‘Elderly’). PERMANOVA analysis of the gut microbiome composition (after adjusting for country and the DNA extraction technique) indicated that the elderly controls had a significantly distinct microbiome composition compared to the young and the middle-aged (p<0.001) (Figure 1B), in line with our previous findings (Claesson et al., 2012). To further explore this effect, we then investigated the effect of subject age on the gut microbiome composition in five diseases, IBD, type II diabetes (T2D), intestinal polyps, CRC, and liver cirrhosis, for which substantial number of cases were available across at least two age-groups. Samples corresponding to different diseases were available in different cohorts (Table 1). Each cohort dataset comprised healthy controls and diseased individuals matched with respect to geographical region and environment (thereby reducing these confounding effects on the gut microbiome), so we were able to perform PERMANOVA to quantify the effect of both age group (‘Young’, ‘Middle-Aged’ and ‘Elderly’) and disease status (‘Control’ and ‘Disease’) on the gut microbiome (separately in each of the different disease-specific cohorts). For all five diseases, age had a significant effect. Notably, for three diseases (T2D, CRC and intestinal polyps), this effect was higher than that of the disease itself (Figure 1C).

Figure 1 with 3 supplements see all
Age influences microbiome composition as well as microbiome-disease signatures.

(A) Bar plots showing the effect (denoted by R2 values computed using PERMANOVA after adjusting for the DNA extraction technique as the confounder) of host factors with microbiome composition in the ExperimentHub repository. Only metadata available for at least 30% of the samples are shown. The p-values for the significance of association are also indicated as ****: p<0.0001; ***: p<0.001, **: p<0.01, *: p<0.05. (B) Principal Co-ordinate Analysis (PCoA) plots of the species profiles of the ‘control’ samples grouped into three age ranges, Young (20–39 years), Middle (40–59 years) and Elderly (60 years and above). The significance (p-value) of the differences between the three groups, computed using PERMANOVA (adonis) after considering the country-specific differences and the DNA extraction technique, is also indicated. The boxplots on the top show the variation of the top three PCoA coordinates for the samples belonging to the three age-groups. The elderly harboured a significantly different microbiome compared to the young/middle-aged. (C) Barplots of PERMANOVA R2 values showing the variation of microbiome with disease (adjusting for age-group) and age-group (adjusting for disease status) in the five disease cohorts. The Cohort-specific analyses ensured that the variations observed were not due to country-specific regional differences in microbiome composition. However, within each cohort, there were skews in the representation of diseased and control samples from different age-groups (as seen in Table 1). Furthermore, in four out of the eight cohorts, there were significant differences in the age variation of control and diseased individuals, as shown by the beanplots in D.

Table 1
Number of control and diseased individuals belonging to the different age-groups present in the continent-specific groups pertaining to each disease.

Age-groups where the number of control/diseased samples are less than 15 are highlighted in red. The shortened notations for the different country used are ESP: Spain; USA: United States, CHN: China, SWE: Sweden, AUT: Austria, FRA: France.

CRC CohortsCountryYoungMiddleElderly
ControlDiseaseControlDiseaseControlDisease
ZellerG_2014FRA5015143331
FengQ_2015AUT004105736
VogtmannE_2016USA2317184139
Cirrhosis CohortsCountryYoungMiddleElderly
ControlDiseaseControlDiseaseControlDisease
QinN_2014CHN64194577526
IBDCountryYoungMiddleElderly
ControlDiseaseControlDiseaseControlDisease
FranzosaCA_2018USA367716414514
NielsenHB_2014ESP35532284128
T2D CohortsCountryYoungMiddleElderly
ControlDiseaseControlDiseaseControlDisease
KarlssonFH_2013SWE00004353
QinJ_2012CHN692789851057
Polyps CohortsCountryYoungMiddleElderly
ControlDiseaseControlDiseaseControlDisease
Feng_2015AUT004848
ZellerG_2014FRA5015134129

Next, we investigated whether these age-related changes in microbiome composition influenced disease signatures. However, performing this investigation posed a specific challenge. Given that regional factors like country or continent had the highest effect on the gut microbiome composition, any investigation into age-group-specific microbiome signatures for the various diseases required the comparisons to be performed within geographically homogeneous sub-populations (or study cohorts) to ensure that any differences in disease signatures were not simply driven by regional variations in gut microbiome composition. However, focusing on specific disease cohorts (which belong to specific countries) also imposes limitations including reduction in sample size (and therefore statistical power) and bias for certain age-groups. Among the eight disease cohorts (in the curatedMetagenomicData) that we analyzed here, we discovered that half of these featured a significant difference in the age of the control and diseased individuals (i.e. they were not age matched) (Figure 1D). In addition, there were skews in the representation of specific age-groups for most of the disease cohorts. Adding ‘control’ samples from other cohorts that were collected from the same region (country or continent) as the corresponding disease cohorts was expected to circumvent this issue by increasing the number of available samples and thereby the statistical power of comparisons. We addressed this issue by grouping the samples into disease-specific country-/continent-level bins (See Materials and methods; Figure 1—figure supplement 3). This ensured sufficient representation of samples from the three different age-groups for all diseases.

Next, we performed investigations using two different approaches to explore the effect of age on disease-microbiome signatures. In the first analysis, within each disease-specific country-level cohort, we performed PERMANOVA investigating the effect of the interaction between disease signatures and age-group, after adjusting for the effects of country (given that there were samples from different countries within the different disease cohorts) and the independent effects of disease and age-group (Table 2). Interaction between two metadata categories measures the extent to which the microbiome variation with respect one metadata (in this case, the disease) is influenced by the variation in the other (that is the age-group). For three of the diseases (IBD, CRC and Polyps), the effect of the interaction of disease with age-group was significant (all with p<0.05; PERMANOVA after adjusting for the confounders as above). The effect was also marginally significant for T2D (p<0.08; PERMANOVA). This indicated that (even after considering the regional variations and the individual influences of disease and age), for these diseases, the microbiome variation associated with disease was significantly influenced by the variations in the age-group of the individuals. Further investigation of these influences (measured by the R2) also indicated that, while IBD and CRC had the highest influence of age as a covariate of disease signatures, with T2D and Polyps relatively lower but significant influences (Table 2).

Table 2
Results of PERMANOVA analysis investigating the effect of the interaction between disease signatures and age-group, after adjusting for the effects of country (within the continent cohorts) and the independent effects of disease and age-group.
AdonisT2DIBDCRCPolypsCirrhosis
F.ModelR-SquaredP-ValueF.ModelR-SquaredP-ValueF.ModelR-SquaredP-ValueF.ModelR-SquaredP-ValueF.ModelR-SquaredP-Value
Country16.690.0290.00111.340.0170.0017.630.0270.0014.650.0220.001NANANA
Disease4.810.0080.00115.790.0230.0014.700.0080.0011.250.0060.02812.1070.0290.001
Age-Group1.380.0050.0012.510.0070.0013.800.0070.0011.000.0040.4081.2390.0060.016
Disease:Age-Group1.120.0040.082.730.0080.0013.570.0060.0011.380.0060.0111.0350.0050.290

In the second analysis, we utilized Random Forest (RF) Classifier Models. Random Forest models belong to a category of ensemble-based machine learning methods (based on decision trees), that can be used to predict a given trait (in this case, disease status) based on a list of predictor features. Random Forests can not only be used to judge the strength of the association between the predictors and the response (based on the accuracy or area under the curve (AUC) of prediction), but also whether associations identified on one set of observations are extrapolatable to another. Consequently, Random Forest models have been routinely used in microbiome based studies to not only quantify and characterize the microbiome associations with various diseases (Feng et al., 2015; Karlsson et al., 2013; Pasolli et al., 2016; Qin et al., 2012), but also to study the transferability of the associations predicted in set of individuals on others (He et al., 2018; Thomas et al., 2019).

We specifically focused on the disease-specific country-level bins (to ensure regional homogeneity as much as possible). Subsequently, we divided subjects (in the country level cohorts corresponding to each disease) into three age bands (as described above) and performed 100 iterations, each time training Random Forest classifiers on a subset of samples belonging to an age band, and evaluating the disease classification performance on the samples from the same age band (excluding the samples used for training) (Same Age-group classification) or the other age bands (Different Age-group classification) (while keeping the training and testing data set sizes constant across age-bands) (see Figure 2—figure supplement 1 and Materials and methods for details). In summary, in each disease-age-group scenario, we used two different approaches to assess whether disease classification performance was significantly different between Same Age-group classification and Different Age-group classification. In the first approach, for the 100 iterative re-sampled classifier models, we compared the AUCs obtained for the Same age-group classification with those obtained for the Different Age-group classification using paired Wilcoxon Signed Rank tests. In the second approach, using Permutation tests, for the 100 iterative re-sampled classifier models, we compared if the actual difference in AUC for the age-group cohorts (Same Age-group classification – Different Age-group classification) is significantly different from what would be expected by random (null distribution generated by permuting the age-group labels of the subjects) (Figure 2—figure supplement 1 and Materials and methods).

The data revealed large differences across age groups for the various diseases. Specifically, in 10 of the 13 disease-age group scenarios (covering five diseases), classifiers trained and tested on the same age-group had significantly higher disease prediction AUC than when tested on different age-groups (Figure 2), with the improvement of classification performance significantly higher than would be expected by random chance (obtained using the permutation test based strategy) (Figure 2—figure supplement 2). This confirmed that microbiome-disease associations had age-centric trends. Furthermore, the extent of the differences was pronounced for IBD, CRC and T2D (the above trend being reflected across all age-groups), reflecting the patterns observed in the PERMANOVA analysis (Table 2). CRC and Polyps were characterized by noticeably similar age-specific trends wherein the elderly age-groups had a noticeable decrease in classification AUCs (that is lower AUCs for classifiers trained or tested on the elderly age-groups), indicating that the microbiome-disease signatures for these diseases in the elderly age-groups are weaker (Figure 2). For these two diseases, the AUC difference between the Same Age-group and Different Age-group disease classification was also reduced in the elderly (in the case of Polyps, this difference was not significantly different what would be expected by random chance) (Figure 2—figure supplement 2).

Figure 2 with 2 supplements see all
Microbiome-disease signatures display specific age group centric trends.

Boxplots showing the variation of disease-classification area under the curve (AUCs) when classifiers trained on one age-group were tested on either the same (denoted as SameAge or Same Age-group classification) or different age-groups (denoted as DiffAge or Different Age-group classification) for (A) IBD (B) T2D (C) CRC (D) Polyps and (E) Cirrhosis. Each point denotes the median AUC (of 20 iterations) obtained using each of the 100 sub-sample based Random Forest classifier models when tested on samples from the Same Age-group (in blue) or Different Age-groups (in red). Median AUC values obtained for the same classifier for Same Age-group and Different Age-group classification are joined by grey lines. Scenarios where in the Same Age-group classification had a significant increase of classification AUC as compared to the Different Age-group are indicated (using the P-values of significance). The Wilcoxon signed rank test p-values of significance, after correction using Holm method, are indicated as ***: p<0.001, **: p<0.01, *: p<0.05.

Age-centric differences in microbiome-disease associations

We next sought to identify differentially associated age-disease markers and how these corresponded with the currently known microbiome-disease associations. Besides quantifying the strength and extrapolatable nature of the microbiome signatures, iterative RF models also provided the list of taxa contributing to the models along with their relative importance scores (for each disease age-group scenario). Therefore, in the first step, based on the RF analysis (as described above) for each disease, we computed the age-group specific feature importance scores for each taxa and then shortlisted the taxa having marker scores in the top 85 percentile individually for each of the age-groups (See Materials and methods; Figure 3—figure supplement 1; Figure 3—source data 1). Comparing the top 85 percentile markers across the different age-groups revealed that the proportion of age-influenced markers (or markers differentially associated across at least one age-group) ranged from 30% in CRC to 50% in IBD (greater than 35% for the other three) (Figure 3—source data 1; Figure 3—figure supplement 2). The feature importance scores (obtained using the iterative RF models) were then compared across age-groups and taxa having significant differences in the feature importance scores (across age-group) (Benjamini FDR < 0.01 Kruskal-Wallis test for IBD, T2D and Cirrhosis and Mann-Whitney Tests for CRC and Polyps) were then identified (Figure 3—source data 1).

These results indicate that the microbiome associations with disease have both an age-group-specific and an age-group-independent component. However, many of the age-group-specific changes in microbiome-disease association may also be reflections of changes that accompany ageing in general. Age had the second major effect on microbiome composition, and it was important to investigate these age-specific changes in disease-microbiome associations after deconvoluting for the effect of ageing. Secondly, while grouping samples into country-level bins ensured enough regional homogeneity of the compared groups, there were still biases in the representation of certain regions in certain age-groups for the different diseases (Figure 1—figure supplement 3). Given that RF models cannot intrinsically adjust for these confounding effects, we probed this using a linear regression-based strategy that compared the extent of influence that age-group had on the disease-association pattern of a taxon (Disease:Age-group) as compared to the individual influences of disease and age-group (see Materials and methods; Figure 3—figure supplement 3). We specifically investigated those taxa having significant differences in their feature importance scores (Figure 3—source data 1) and identified those having significantly higher influence of Disease interacting with Age-group than Age-group alone (Log likelihood test one sided p<0.05) (Figure 3—source data 2; Figure 3) as the final validated list of ‘strictly age-specific disease markers’. Notably, the percentage of taxa showing significant differences in their feature importance scores that were also validated in the Linear regression-based approach, showed disease-specific trends reflecting those observed in our PERMANOVA-based (Table 2) and RF-based analysis (Figure 2). Specifically, for IBD and CRC, more than 63% of features showing significant differences in their marker scores across age-groups were also validated in the linear-regression-based validation, indicating that for these diseases, a majority of age-group-specific disease associations persist even after accounting for the overall effect of ageing. In contrast, for Polyps and Cirrhosis these overlaps showed a progressive decrease (Figure 3—figure supplement 4A).

Figure 3 with 4 supplements see all
Specific taxa show age-group linked trends of disease association.

Heatmaps showing the marker scores for the list of taxa that are differentially associated with the indicated disease across the age-groups (Y: Young; M: Middle-aged and E: Elderly). For each disease, this list of species was selected as those which were among the top 85 percentile features in at least one age-group and which displayed significant variation in their feature importance scores across at least two age-groups. These taxa were further validated using a linear regression approach to ensure that their age-group specific association with disease was significant even after accounting for the independent changes associated with ageing. The font colors of the species indicate whether the species were reported in the original studies as being associated with the given disease (Dark blue: Associated Previously; Black: Not Associated). For each disease, heatplots (adjoining on the right side of the corresponding heatmap) shows the different taxa were identified within the top 85 percentile markers for each age-group (in blue color).

We next compared the age-linked disease markers identified here with the disease markers originally reported. For four of the diseases (i.e. not CRC), we compiled a list of known disease-associated markers from each of the original studies (Feng et al., 2015; Franzosa et al., 2019; Karlsson et al., 2013; Qin et al., 2012; Qin et al., 2014). For CRC, a recently study presented a meta-analysis of all major cohorts (encompassing those in curatedMetagenomicData) (Feng et al., 2015; Vogtmann et al., 2016; Zeller et al., 2014), along with three newly sequenced cohorts (Thomas et al., 2019), to produce a refined set of cross-cohort validated CRC markers. We utilized this list to compile the set of known markers for CRC. Comparing the list of these associations with a pre-compiled list of all known marker associations reported in the original studies (Figure 3—source data 3) (Feng et al., 2015; Franzosa et al., 2019; Karlsson et al., 2013; Qin et al., 2012; Qin et al., 2014; Vogtmann et al., 2016; Zeller et al., 2014), identified that 40% of taxa identified in the original study for Polyps, more than 63% for CRC and IBD, 58% for T2D and 25% for Cirrhosis, displayed significant differences in their feature importance scores across age-groups, thereby emphasizing that age-linked disease-microbiome associations are prevalent across diseases (Figure 3—figure supplement 4B). Even in the final list of ‘strictly age-specific markers’ that were deconvoluted for the effect of ageing, the overlap varied from 20% for T2D and Polyps to as high as 63% for CRC (Figure 3; Figure 3—figure supplement 4C). The effects were especially pronounced for IBD, where many of the known markers (species belonging to Lachnospiraceae, Escherichia, Clostridium clostridioforme, Clostridium nexile, Blautia producta) were not even identified in the top 85 percentile in certain age-groups (Figure 3). For CRC, some of the well-known markers Fusobacterium nucleatum, Parvimonas micra, Peptostreptococcus stomatis, and Porphyromonas asaccharolytica, although identified in the top 85 percentile across age-groups, the strength of the associations in terms of RF feature importance scores as well as the results of linear modelling showed age-specific trends. The same pattern was also observed for some of the signature taxa for T2D (Faecalibacterium prausnitzii, Roseburia intestinalis, Haemophilus parainfluenzae, Bacteroides intestinalis and Alistipes indistinctus) and Polyps (Bacteroides dorei). The variations in the strength and stability of these associations were further evident when we compared the percentage of times each marker is identified with scores greater than 85 percentile in different age-groups (within the 100 iterations for each age-group) (Figure 3—figure supplement 4D). Several known markers of CRC (Parvimonas micra, Eubacterium eligens) and T2D (Bacteroides intestinalis, Haemophilus parainfluenzae) are identified in the top 85 percentile across more than 80% iterations in certain age-groups, but less than 50% in others. This has implications for the efficacy of microbiome-directed diagnostic/therapeutic strategies across age groups.

Replicability of the age-centric microbiome-disease signatures across multiple cohorts in CRC

We next tested if the differential trends of disease association observed were affected by cohort-specific differences. For CRC, we observed the strongest effects of age-group influenced disease signatures which affected more than 63% known CRC markers, and that were validated even after considering country-specific representations and deconvoluting the effect of ageing. Furthermore, the three newly sequenced cohorts (referred to as ‘Cohort1’, ‘Cohort2’ and ‘WirbelJ_2019’, as a group ‘Validation Cohort’) had sufficient representation of samples from different age-groups (Supplementary file 1) (Thomas et al., 2019; Wirbel et al., 2019), and represented new data that had not been included in our initial identification of differentially associated markers. In the curatedMetagenomicData repository, the CRC disease samples were collected from three cohorts, namely ZellerG_2014, VogtmannE_2016 and FengQ_2015 (Feng et al., 2015; Vogtmann et al., 2016; Zeller et al., 2014). To explore the reproducibility of the associations (irrespective of the general effect of ageing) as well as to understand the biological basis for these differential associations, we validated the age-aware RF classification models designed on this cohort (referred to as the Training Set1) on the validation cohort (Figure 4A; upper panel) (using a similar strategies as described in Figure 2—figure supplement 1). The age-specific trends of disease classification were similar to those obtained earlier (Figure 2), that is, classifiers trained on the young/middle-aged individuals had the highest classification performance when tested on the same age-group, with a significant reduction in classification AUCs when tested on the elderly. In contrast, performance decreased significantly for those either trained or tested on elderly (where the difference between the Same Age-group and Different age-group testing was not observed to be significant) (Figure 4A; Figure 4—figure supplement 1). We further re-generated similar iterative age-group specific microbiome disease-prediction models by taking subsets of samples from within the ‘Validation Cohort’ (referred to as Training Set2) and testing on non-training subsets of the same. The classification pattern remained invariant (Figure 4A; lower panel versus upper panel). These results indicated that the variations in age-group specific microbiome-disease signatures remained stable and were not derived from biases in the training datasets.

Figure 4 with 1 supplement see all
Age-dependent CRC-specific markers are reproducible across multiple cohorts and ageing-associated changes make the elderly gut microbiome disease-like.

(A) The boxplot on the top panel shows the distribution of AUC values obtained when classifiers trained on different age-groups (YM: Young/Middle-aged; E: Elderly) in three cohorts of the curatedMetagenomicData (Training_Set1: ZellerG_2014, FengQ_2015 and VogtmannE_2016) are tested on the three datasets of the validation cohort (ThomasAJ_Cohort1, ThomasAJ_Cohort2 and WirbelJ_2019). The lower panel shows the same, but with age-group specific classifiers trained from within the validation cohort (Training_Set2). Both the classification models generated the same trends of classification, indicating age-group specific reproducibility of the disease signatures. The description of the point colors is the same as for Figure 2. (B) Age-group dependent associations of the known CRC markers in the two independent cohorts, namely Training_Set1 (curatedMetagenomicData) and Training_Set2 (Validation Cohort). Shades of blue indicate higher feature importance scores in the young/middle-aged and red indicates higher feature importance scores in the elderly. FDR p<0.15 indicates features identified as being high either in elderly of young/middle with Benjamini-Hochberg corrected Mann-Whitney test p-value<0.15. FDR p<0.25 indicates features identified as being high either in elderly of young/middle with Mann-Whitney test p-value<0.25. Out of the 19 known and validated CRC-markers (obtained from Thomas et al., 2019), 13 showed significant differences in their feature importance scores across the two age-groups (in the curatedMetagenomicData cohorts). For nine of these 13 markers, the pattern of associations could be reproduced in the Validation cohort, further indicating the replicability of the obtained results. The feature ranks of the top 10 markers obtained in Thomas et al. (2019) are also shown. Six of the top 10 markers show increased association, but only within the young/middle-aged. Only one of the markers associated with the elderly. This indicates a loss of disease-signature in the elderly. (C) Across cohort Spearman distances of feature rank profiles obtained for the disease classifiers trained on the different age-groups (See Materials and methods). A stable disease signature would result in reproducible species rank profiles across cohort and consequently lower Spearman distances. While this is the case for young/middle-aged, the elderly signatures obtained for the different cohorts show significantly high Spearman distances (showing significant variations and lack of disease signature). (D) The log ratios of the prevalence rates of the top six CRC-associated markers in elderly controls with respect to the young/middle-aged controls (in both the curatedMetagenomicData and CRC-specific cohorts). A positive value indicates higher prevalence rates in elderly controls. The significance of the increase is also indicated (p-values of fishers’ exact test combined using Fisher method) as ***: p<0.001, **: p<0.01, *: p<0.05. The increase in the elderly is characterized by a significant decrease in the effect-size differences between the controls and diseased in elderly, leading to masked signatures.

We then examined the overlap of the age-dependent associations of the known CRC markers across the two training models (Figure 4B). From among the refined cross-cohort associated subset of 19 CRC-associated taxa (Thomas et al., 2019), 13 (67%) had differential association with either of the age-groups (with FDR corrected p-value<0.1) in Training Set 1. Nine of these (77%) could be replicated with the same directionality in the Training Set 2 (Figure 4B). Interestingly, from among the top CRC-predictive features reported in the recent meta-analysis, six were found to be associated with higher predictive power only in the young/middle age category (Figure 4B). Only one was observed to be associated with the elderly microbiome.

We next checked the stability of the feature-associations of CRC across the young/middle and the elderly age-groups across the cohorts. The bootstrapped approach adopted in this study enabled obtaining the microbiome-disease signatures for multiple subsets of diseased and control subjects. In a scenario where the microbiome signature is robust and stable, the microbiome-disease associations (which in this case are obtained as the feature rank profiles for each of the taxa) from each iteration of classifier should be similar (across the cohorts). However, in this specific case, while the classifier profiles for the young/middle-aged individuals across the two cohorts were relatively similar, the inter-training-cohort distances obtained for the elderly-specific disease classifiers were significantly higher (indicating variable disease signature and loss of reproducibility) (p<2.2e-16; Mann Whitney Test) (Figure 4C).

Although no specific trends were observed when comparing abundances of the known CRC-specific markers, five of the top ten markers (Fusobacterium nucleatum, Peptostreptococcus stomatis, Gemella morbillorum, Parvimonas micra and Parvimonas spp) showed significantly higher prevalence rates in the elderly controls (reproducibly across both datasets) (Figure 4D). This resulted in a significant reduction in the effect size of their differences (between control and diseased individuals) in the elderly age-group. Thus, ageing-associated changes may render elderly individuals susceptible to specific microbiome-related diseases like CRC.

Age-specific changes in the directionality of taxon abundance alterations for specific diseases, and the microbiome response shared by multiple diseases

We next investigated if the diseases were characterized by distinct patterns of microbial taxon gain or loss, even across the different age-groups. We first focused on the individual study cohorts (Figure 1C). For each disease-age-group scenario, we determined the directionality (increased versus decreased in disease) of association of the corresponding top disease-predictors by comparing their abundance trends in (study-matched) control and diseased samples (Mann Whitney Test p<0.05). For four of the five diseases (i.e. except polyps), there was a significant change in the directionality of the microbiome alteration, characterized by a significant reduction in the number of gained features with older age (Fishers’ exact test p<0.05; Figure 5—figure supplement 1). To add statistical power and check whether the above trend is retained across a larger cohort, we performed the analysis using ‘continent-matched’ disease and controls (using reasonably stringent thresholds of Benjamini-Hochberg corrected P value < 0.1 obtained from Mann Whitney tests). The trend remained similar. For elderly, microbiome alterations (for all diseases except cirrhosis and polyps) were characterized by a significant increase in the number of lost taxa (Figure 5A). The list of disease-specific markers with significant directionality of disease association across age-groups are presented in Figure 5—source data 1. Thus, the microbiome alterations in most of these diseases are characterized by a gradual shift from a state dominated by gained microbiome components to an increasing loss of control-associated taxa in the elderly.

Figure 5 with 2 supplements see all
Age-related microbiome changes affect taxon abundance alterations for specific diseases, as well as the microbiome response shared by multiple diseases.

(A) Comparison of the relative proportions of more abundant and less abundant disease-specific marker taxa across the young, middle-aged and elderly age-groups for the five diseases. For each disease-age-group scenario, we checked for the directionality (increased abundance in disease v/s decreased in disease) of association of the corresponding top disease-predictors by comparing their abundance trends in the control and diseased samples belonging to the specific age-groups (See Materials and methods). To ensure that the results thus obtained were not affected by regional variations in microbiome composition, we again restricted these comparisons to the disease-specific continent cohorts. (B) Comparison of the disease prediction AUCs, the disease classification sensitivity and control classification specificity of generic disease prediction models obtained for the elderly and young/middle-aged groups. Overall, the generic disease classifiers had a significant decrease in performance in the elderly age groups, indicating that shared microbiome response may be reduced in the elderly. Moreover, the loss of performance was especially significant with respect to the discrimination of control samples from disease (C) Heatmap of marker species showing consistent trends of either increase or decrease in at least two diseases in the elderly and young/middle-aged groups. Blue indicates consistent increase in two or more diseases, red indicates decrease in two or more diseases. Based on their patterns of increase or decrease across the two age-groups, the taxa could be classified into six groups, namely G1-G3 and L1-L3.

The overlap of altered taxa across diseases reported in one study was 51% (Duvallet et al., 2017), while another study reported that generic control versus disease classifiers trained by agglomerating disease samples from multiple studies could still distinguish controls from disease with an AUC of greater than 0.8 (Pasolli et al., 2016). Given that microbiome composition changes with age, we investigated the effect of age on the extent of these shared disease responses. We designed generic disease classifiers (using Random Forest), taking equally sized sub-samples of controls and diseased individuals (containing equal number of samples from each disease to prevent disease/age-group specific biases in classification performance) (See Materials and methods and Figure 1—figure supplement 3). While the performances of the generic disease prediction models in the young/middle-aged was high (median AUC: 0.79) and similar to those reported by earlier studies (Pasolli et al., 2016), the same models applied to data from elderly subjects had significantly lower performance AUC (p<1e-7) (Figure 5B). Moreover, in these models, while no significant differences were observed with respect to the disease prediction sensitivities (p<0.13), the specificity of prediction (that is the accuracy of identifying healthy individuals) was significantly lower for the elderly. This was not an effect of the differential representation of samples from the different diseases, as we had ensured equal representation of all diseases across all age-groups. Thus, in contrast to previous meta-analyses, the shared disease response was significantly lower across elderly subjects, primarily with respect to the discrimination of non-diseased individuals. Furthermore, our clarification of the effect of age on disease-associated taxa (Figure 3—source data 1; Figure 3—source data 2; Figure 5—source data 1) provides a refined set of features for improved microbiome-based diagnostics for these diseases. The above disease-independent changes in the pattern of microbial alterations as well as the inability of the generic disease classifiers to distinguish non-diseased controls were intriguing and could be a consequence of the loss of beneficial bacteria in the gut microbiome with ageing, which in turn could be driven by a multitude of factors like diet and medication. This increasing loss could lead to dysbiotic configurations characterized by higher inter individual variability, increased abundance of pathobionts (resulting in loss of disease signature) thereby making the microbiome more susceptible to diseases. Therefore, using intra age-group Spearman distances, we next checked whether the samples from elderly controls had significantly higher variability as compared to young/middle-aged controls, across the different continental regions (Figure 5—figure supplement 2). In line with our hypothesis, for both Europe and North America, the gut microbiome from elderly individuals was significantly more variable compared to young and middle-aged controls. However, this was not observed in the Asian (Chinese) cohort. Interestingly, the cirrhosis group neither shows an association with disease signatures nor the gain versus loss pattern contained only subjects from Asia.

Next, we sought to characterize the elements of the shared disease response. Notably, closer inspection of the directionality of the associations indicated specific taxa with consistent trends of association with multiple diseases (based on the trend shown in Figure 5—source data 1). The overall patterns of taxon gain or loss encompassed several trends observed by earlier studies (Duvallet et al., 2017; Pasolli et al., 2016). For example, Streptococcus anginosus and Fusobacterium nucleatum were detected as gained in multiple diseases. Similarly, species belonging to Roseburia spp. (R. hominis) were lost. We identified a total of 61 taxa that showed consistent directionality of association with multiple diseases in either young/middle-aged or the elderly age-groups (Figure 5C). Based on their differential detection profiles in the shared response across age-groups, we assigned these into six different groups, namely G1 (increased in disease across all age groups), G2 (increased in disease only in the elderly), G3 (increased only in young/middle-aged), L1 (decreased in disease across both), L2 (decreased only in the elderly) and L3 (decreased only in the young/middle-aged groups) (Figure 5C). Many of the species previously reported as associated with shared gain or loss across multiple diseases (Duvallet et al., 2017; Pasolli et al., 2016) belonged to the G3 group, that is, they showed similar trends of gain or loss in disease (as reported earlier) in the young and the middle-aged groups, but not in the elderly. These included the Streptococci, Fusobacterium nucleatum, and Escherichia coli and Bacteroides fragilis. In contrast, a separate group of species including Ruminococcus torques, Solobacterium moorei and Streptococcus mitis were associated with multiple diseases only in the elderly. Finally, we identified a distinct group of species (G1) that was gained across diseases in both elderly and young/middle aged groups. These included a group of Clostridia (C. bolteae, C. symbiosum, C. hathewayi, C. citronae, C. asparagiforme) (Figure 5C; see Figure 5—source data 1 for individual diseases). These taxa have been identified in separate studies of different diseases and/or disease-like states (T2D, Polyps, CRC and autism) (Pequegnat et al., 2013; Qin et al., 2012; Sinha et al., 2019; Yu et al., 2017), but are shown here, for the first time to be part of a shared gain response across diseases. Based on these findings, we hypothesize that this specific G1 group of taxa constitute a shared disease response associated with a general patho-physiological failure in the affected individual.

Reproducible association of the G1 disease-positive markers with increased frailty in elderly individuals from the ELDERMET cohort

Frailty in the elderly is characterized by reduced function of multiple systems. We investigated if the taxon associations with frailty could be validated in the ELDERMET cohort (Supplementary file 2), for whom we had both shotgun metagenome and faecal metabolomic data. Using Random Forest regression (with five-fold cross validation), we could predict the frailty of an individual (testing both community- and residential care-dwelling subjects) based on the taxonomic composition of the gut microbiome (R = 0.79; Figure 6A; Figure 6—figure supplement 1). We then calculated the mean feature importance ranks in the frailty prediction model for the six gain/loss generic disease response groups (from Figure 5C). Validating our previous observations, the G1 group of taxa had the highest mean rank of feature importance scores for frailty (Functional Independence Measure (FIM)) prediction, indicating that this group had the highest predictive power for frailty in the ELDERMET subjects (Figure 6B), followed by the G2 group (increased across multiple diseases only in the elderly). Validating our earlier findings on the elderly-specific markers of shared microbiome response, this was followed by the L1 group. An optimal number of eight taxa had the highest frailty predictive ability (Figure 5C; Figure 6C; Figure 6—figure supplement 1). All these taxa were more abundant with frailty (negatively correlated with FIM and positively associated with Frailty) (Figure 6—source data 1A–B). Five of the eight taxa belonged to the G1 group (Figure 6C; Figure 5C). Thus, these findings independently validated in the ELDERMET cohort our earlier identification of specific elderly-associated generic disease response groups (Figure 5C).

Figure 6 with 3 supplements see all
Frailty-associated markers have shared positive associations across multiple diseases in both age groups and have a specific metabolic signature.

(A) Actual FIM values versus FIMs predicted by Random forest of microbiome features of the elderly individuals of the ELDERMET cohort living in Community or Residential care (Longstay). (B) Mean ranks of the various taxonomic groups (identified in Figure 3) for the prediction of FIM (an inverse measure of frailty) in the ELDERMET cohort. (C) Variable Importance Scores of the eight markers with the highest predictive power in the Random Forest models for prediction of FIM. A comparison of the abundance of markers between HighFIM and LowFIM individuals indicated that all of these markers were associated with frailty state. (D) The network in the central panel indicates the 13 metabolite profiles significantly associated with the top markers. Taxon markers are indicated in the center. Consumption profiles are in the upper half (in pink octagons) and Production profiles are on the lower panel (in yellow octagons). Edges indicate presence. Second from the left in top panel are the correlations between predicted and actual FIM values obtained for iterative bootstrapped Random Forest models (training on 20% and testing on the rest 80%) using only the 13 metabolite profile markers of (D), all metabolite profiles and all metabolite profiles removing the 13 metabolite markers. Top and bottom panels show the validation (indicated by arrows) obtained for the predicted metabolite markers using either the measured metabolites, dietary consumption profiles, specific microbial pathway abundances as well as the CutC gene family abundances identified using humann2 (shown either as boxplot comparing the profiles between Frail and Non-Frail individuals or scatterplots showing correlations between the measured metabolite level and the FIM value of the individuals). A total of 11 of the 13 metabolites could be validated using either of these strategies.

If microbiota alterations contribute to disease causation, microbial metabolites may be effector molecules. A distinguishing feature of the current study is that it is based on metagenomic data, as distinct from previous 16S-based meta-analyses of microbiome-disease (Duvallet et al., 2017). This allowed us to obtain taxonomic composition at finer taxa level, which in turn we could use to predict metabolite profiles of the microbiomes by exploiting experimentally validated functional profiles of the constituent taxa. For this purpose, we used the Virtual Metabolic Human database (Noronha et al., 2018), as well as a recent method to search experimentally-verified functional profiles representing the production and consumption patterns of different metabolites in various gut-associated species (Sung et al., 2017). We could thus collate more than 300 metabolic profiles (consumption/production/degradation) from 992 species (Figure 6—source data 2). A total of 82 metabolite profiles were observed to have significant association with FIM scores (Spearman Rho; FDR of less than 0.25) (Figure 6—figure supplement 2). We observed that this association analysis of metabolite profiles reflects frailty-associated changes with respect to bioavailability of specific compounds, many of which have been previously shown to have corresponding associations with health, thereby validating our findings (Claesson et al., 2012). Specifically, the onset of frailty is observed to be associated with an increase of SCFA consumption by gut-bacteria (accompanied by a concomitant decrease of the production of the SCFA butyrate), increased consumption of the beneficial amino acid Tryptophan as well as the increased production of the T2D-linked branched chain amino acid Threonine. We first focussed on the group of frailty marker taxa and identified 13 metabolic profiles (Figure 6D) that were significantly associated with the eight frailty-marker taxa (See Materials and methods). This subset of 13 metabolite profiles alone could predict frailty with a R value of 0.60 (between the actual and predicted FIM values), which is significantly higher than that obtained after removing these features from the metabolite profile (Figure 6D; Top panel second from left). The first set of key metabolic profiles included the degradation of primary bile acids, namely cholic acids (CA) and chenodeoxycholic acids (CDCA) to produce hydrophobic secondary bile acids (lithocholic acid: LA; deoxycholic acid: DCA). We validated this predicted metabolic functionality against the experimentally measured fecal metabolomic profiles for ELDERMET subjects, where the fecal metabolomes of frail individuals were characterized by significantly higher levels of LCA (along with its derivatives) and DCA (p<0.006) and significantly lower levels of CA and CDCA (p<0.02), as compared to the non-frail individuals (Figure 6D). The two species associated with this functionality, namely Clostridium scindens and Clostridium leptum, did not belong to either the G1 or G2 groups of species (contrary to their frailty-association in this study). These species were previously implicated in Clostridium difficile resistance and IBD, respectively (Buffie et al., 2015; Manichanh et al., 2006). Contrary to their therapeutic relevance, this specific functionality of these species, namely production of higher levels of hydrophobic bile acids, has also been associated with the onset of CRC and non-alcoholic fatty liver disease (NAFLD) (Tsuei et al., 2014). One of the recent meta-analyses of CRC gut microbiomes also predicted increased production of secondary bile-acids to be a key CRC-specific functional signature (Wirbel et al., 2019).

Two other metabolite functionalities associated with the frailty-specific markers that were also validated in the fecal metabolome profile were p-cresol production (measured p-cresol negatively correlated with FIM: p<0.01; R = −0.29 with FIM across community and longstay; R = −0.47 for only long-stay) and ethanol production (significantly higher levels in the fecal metabolome of frail individuals in the long-stay cohort, as compared to the non-frail individuals: p<0.02). While p-cresol is a cytotoxic compound (produced by specific gut bacterial species including C. difficile) that is associated with microbiome alteration, large bowel cancer and insulin resistance (Fau et al., 1976; Khan et al., 2014), high level ethanol production in the gut has not only been linked to incidence of NAFLD, atherosclerosis and SIBO, but also to an increase in the levels of inflammatory markers (Elshaghabee et al., 2016). Similarly, production of acetone, ammonia (NH3) have been associated with increased cytotoxicity, small intestinal bacterial overgrowth, as well as insulin resistance (Baskaran et al., 1989; Ghoshal et al., 2017; Khan et al., 2014). Although fecal metabolome data was not available for ExperimentHub microbiome samples, the abundance of specific microbial pathways (obtained using humann2) was associated with the production of these metabolites, namely propane-1,2-diol to acetone, and allantoin to Ammonia/CO2/Glyoxylate was predicted to be significantly higher in the microbiome of the frail individuals (Figure 6D; Lower panel). Another functionality associated with the frailty markers was choline consumption and Trimethylamine (TMA) production. Specific gut microbial species including C. citroniae, C. clostridioforme and C. hathewayi degrade choline to trimethylamine, which is liked with atherosclerosis (Martínez-del Campo et al., 2015). Profiling the abundance (normalized by sequencing depth) of the specific bacterial CutC enzyme catalysing this conversion revealed significantly higher representation in the gut microbiome of the frail individuals (Figure 6D; Upper right panel). This association with CRC has also been reported in one of the meta-analysis datasets used in the current study (Thomas et al., 2019). Thus, there is a prima facie mechanistic case whereby this group of microbiome markers could functionally drive the host to an increasingly pan-disease susceptible state, also accelerating the onset of frailty. Interestingly, identifying similar metabolite associations across the G1-G3 markers indicated overlapping metabolite production signatures of the frailty-associated markers with even the G3 groups of taxa (specifically the production of Ethanol and NH3), indicating that the different taxa groups enriched in a generalized microbiome-disrupted state, irrespective of differences in their composition, contain certain metabolic signatures that may drive the intestinal eco-system to a state detrimental to the host (Figure 6—figure supplement 3). Similarly, comparing the metabolic signatures of the taxa in the G1-G3 groups with those of the different groups of lost taxa L1-L3, also revealed an interesting pattern whereby the ‘gained’ groups were disproportionately associated with the consumption of simple sugars (lactose, psicose, xylose, sucrose, raffinose). The ‘lost’ groups in contrast were enriched for consumption of dietary prebiotics like xylo-oligosaccharides, fructo-oligosaccharides, inulin and production of butyrate and succinate. A recent study of the gut microbiome of Thai individuals migrating to the US observed the loss of metabolic functions linked to degradation of dietary fibers like xylan, arabinoxylan, cellobiose, pullulan, glucomannan and resistant starch, with increasing duration of stay leading to a broader loss of microbiome function (Vangay et al., 2018). These patterns, reflective of the metabolic capabilities of specific microbial groups, are especially important for diet-based microbiome restoration strategies in the elderly, where loss of control-associated taxa has a stronger effect on disease, as compared to the young and the middle-aged.

Discussion

By identifying specific age-linked microbiome associations for different diseases, this study can potentially inform the development of microbiome-based diagnostic strategies customized for specific age-groups. Accounting for age in disease-microbiome classifiers has clarified the microbiome alterations common to many diseases and shortened the lists of taxa specifically implicated in common non-communicable diseases. Furthermore, distinct changes observed in the overall directionality of taxon associations (e.g. the gradual shifting to a loss-of-taxa phenotype in the elderly) highlight that microbiome restoration strategies may be more crucial for disease amelioration in elderly subjects (as opposed to antibiotic-based eradication regimens). This is especially important since specific changes are observed with ageing that can potentially make the host more susceptible to certain microbiome-related diseases. The above pattern was further observed in our analysis using generic disease classifiers. The specific loss of the ability of the classifiers to distinguish between cases and controls further indicates that with ageing, there is loss of health-associated microbiome signatures. Interestingly, these findings resonate with the results of one of our earlier studies, where with increasing age, a decrease of the core microbiome accompanied by an increase of pathobionts was observed (O'Toole and Jeffery). Finally, reproducible identification of a specific set of species markers associated with multiple diseases and their specific metabolite profile (sugar consumption and the production of a range of metabolites that are detrimental for the host) indicates that the acquisition of this subset of disease-associated taxa can shift the metabolic state to a disease-like state.

Separating correlation from causation in microbiome-disease studies requires identification of putative taxa correlating robustly with disease, which will be improved by accounting for age in microbiome-disease association studies. Many of the features in the current analysis were not reported in the original publications. There could be several reasons for this, namely variation in the comparative analysis protocol used in the original studies as compared to those in the current study; more power and robust signatures in the current analysis by virtue of combining multiple studies; study-wise biases in the number of samples belonging to different age-groups. In this regard, a key aspect is the relative stability of the age-specific differential associations across regions/ethnicities. In this study, we have used a combination of Random Forest, linear models as well as validations at different levels of regional homogeneity to not only reduce the effects of regional variations on microbiome signatures but deconvolute the effect of general ageing on these age-specific disease signatures. However, biases in number of samples for certain regions in specific disease-age-group scenario still exist. Currently, a majority of datasets in the curatedMetagenomic data repository and the other validation cohorts are from North America, Europe and China. However, future availability of microbiome data from disease cohorts especially from Non-Western populations will further clarify the age-specific trends of disease classification. This is especially important, since in the current study, we identify region-specific changes across age-groups, where in, while the gut microbiomes of elderly controls from North America and Europe have significantly higher variation as compared to region-matched young/middle-aged controls, no such differences are observed within the Asia (Chinese) populations. Future availability of new disease-specific shotgun microbiome datasets will further enable cross-cohort validations of disease signatures (as was performed in the current study for CRC) on other diseases. Another factor that needs to be considered is the effect of diet. Many of the taxa that are depleted in disease are also taxa whose growth is promoted by complex carbohydrate consumption. Different populations have specific dietary patterns and these associations need to be factored into suitably designed future prospective studies.

We could not control for medication intake in subjects/data from the ExperimentHub, because this data was not available (except for antibiotic treated subjects that were removed), and which can be a major confounder (Forslund et al., 2015). Previous studies have reported the association of some of the disease markers with medication intake. For example, Streptococcaceae have been shown to be associated with intake of Proton Pump Inhibitors (Jackson et al., 2018). Another recent study investigating the effect of metformin on the gut microbiome of two of the T2D cohorts analysed in the current study identified Lactobacillus salivarius to be affected by metformin, accompanied by an increase in Escherichia (Forslund et al., 2015). However, none of the other G1 and G2 markers were reported to be affected. The FranzosaEA_2018 dataset (added along with the curatedMetagenomicData repository) contained drug usage information for Immuno-suppressants, Steroids and Mesalamine usage (Franzosa et al., 2019). For these three drugs, we checked whether the abundance of shared disease markers was affected by medication intake (Supplementary file 3). We could only identify associations for Peptostreptococcaceae (decreased in mesalamine treatment), Coprococcus comes (increased in mesalamine treatment) and Barnesiella intestinihominis (decreased in steroid treatment). Moreover, adjusting for medication did not alter abundance of taxa predictive of frailty in the ELDERMET cohort (for which the medication usage data was available). Each of the top frailty-associated markers were not only observed to have significant associations with the different frailty measures even after taking into account for the effect of the selected sets of medication type, but also identified in the top 15 predictors of frailty, independently in the individuals with high or low medication (Figure 5—source data 1c). However, the lack of associations with this limited list of medications does not completely rule out the possibility of medications being partly responsible as a driver for these differential associations. Consequently, for diseases like T2D, the results obtained here have to be treated with caution. Future cohort studies should record all measurements of diet and medication intake, so that the complex interactions of age, lifestyle, diet, microbiome and health may be further elucidated. Another confounding factor may be age of disease presentation. For example, a recent study identified cancer stage-microbiome interactions (Yachida et al., 2019); however, we also note from the published data that 51% of the younger subjects (less than 60 years of age) presented with Stage III/IV cancer, whereas a significantly higher percentage (71%) of older subjects presented with Stage I/II cancer (p<0.01). Variation in disease presentation in different age groups could also influence the disease-microbiome signatures, and therefore needs to be considered in future studies.

The last concern is methodological and pertains to the non-independence between the training samples in the iterative RF classifiers generated for each disease-age-group scenario. Classifiers generated in each iteration are likely to share overlap of training (and/or testing) samples, thereby potentially resulting in narrower AUC distribution which could inflate significance values. However, the magnitude of the differences as well as consistency of the results (using multiple methods and study cohorts) clearly indicate the reliability of the results obtained in this study.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
Software/AlgorithmcuratedMetagenomic-DataPasolli et al., 2017
Available as a R-library
Version as in Oct, 2018
Software/AlgorithmR packages:
1. randomForest
2. pROC
3. lmtest
4. ade4
5. vegan
6. dunn.test
Availabe as R packages from CRAN.Latest versions as on Oct, 2018

Data collection from the curatedMetagenomicData repository

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Since the focus of investigation was the gut microbiome, we first selected a subset of the curatedMetagenomicData, containing 4195 stool samples (annotated as ‘body_site’: stool) (Pasolli et al., 2017). We subsequently removed the samples which have not defined age and study-condition, thereby filtering the dataset to 3580 samples. From this set, we removed samples having age less than 20 years of age (retaining 2564 samples). Notwithstanding the uniform bioinformatics analysis strategy applied to this data, two major factors that may contribute an artefactual bias in multi-cohort microbiome datasets (and which were available in the metadata) are the read-length (obtained from the sequencer) and DNA extraction methodologies (which are study-specific). To test the effect of these factors, we first removed the samples from Peruvian, African and Fijian individuals in order to remove the confounding effects of region/life-style-specific), along with those from hospitalized individuals. Subsequently, on the remaining subset, we evaluated the effect of these factors using envfit on the species level profiles by first visually comparing the differences using PCoA and then testing the confidence of these differences using envfit (https://cran.r-project.org/web/packages/vegan/vegan.pdf). For this purpose, we performed 20 boot-strapped envfit iterations, each time taking a subset of samples (sub-sample size: 200) and computing the R2 and the significance (P-value) of the differences, and then comparing the distribution obtained with that obtained using a null distribution obtained by taking 200 sub-samples (after permuting the labels). We established that while read lengths had a marginal effect (R = 9e-3, p<0.09) (Figure 1—figure supplement 1A), samples from one of the studies (SchirmerC_2016) (Schirmer et al., 2016), using a DNA extraction technique tagged as ‘Illuminakit’ in the metadata, had a distinct taxonomic profile. We removed the 465 samples of this study from all further analyses, thereby reducing the effect of extraction methodology on taxonomic profiles (p<0.06; Figure 1—figure supplement 1B–C). To this compiled list, we added the samples from four recently published datasets (one IBD-specific dataset of 220 samples, referred to as ‘FranzosaEA_2018’ [Franzosa et al., 2019]; three CRC-Specific datasets referred to as ‘WirbelJ_2019’, ‘ThomasAJ_Cohort1’ and ThomasAJ_Cohort2’ [Thomas et al., 2019; Wirbel et al., 2019]). This repository, along with the 189 shotgun sequenced samples from the ELDERMET cohort, resulted in a total of 2564 samples. Schematic representation of the workflow used for preparing a core set of 2564 gut metagenomic samples derived from the publicly available datasets (curatedMetagenomicData and the four newly added cohorts) and the ELDERMET repository is provided in Figure 1—figure supplement 2. The details of the samples belonging to the curatedMetagenomicData and FranzosaEA_2018 dataset included in the current study are provided in Supplementary file 1. The clinical metadata of the ELDERMET samples are listed in Supplementary file 2. In this analysis, the taxonomic composition obtained using the metaphlan2 pipeline was obtained at the microbial species level. We have used the term ‘species’ and ‘taxa’ in this manuscript to refer to a taxonomic unit below the level of genus.

Effect of host-associated factors, including age groups on the microbiome profile

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We specifically investigated the metadata of the samples listed in Supplementary file 1. Some of the metadata were observed to be redundant and had similar associations (examples included groups like Country and Dataset Name; Age and Age-category; Study condition and Disease; Antibiotics current use and Antibiotics family). In these cases, we retained the former metadata and removed the latter ones. We added another region-specific metadata, namely Continent, for reasons explained in the subsequent section. Subsequently, we filtered out those metadata present in less than 30% of the samples. A total of six metadata remained. We obtained the association of each of these metadata using PERMANOVA (using the adonis function of the vegan R package). Given that DNA extraction methodologies still had a marginal effect on the gut microbiome composition (R2 = 0.019), the PERMANOVA analyses for each of the metadata were performed after adjusting for the DNA extraction method as a confounder. The PERMANOVA analysis (for each metadata) was performed using the adonis function of the vegan package using the following pseudocode formula: adonis(species ~ dna_extraction_method + metadata).

For investigating the variation of the microbiome with age across the adult-hood landscape, the individuals were binned into three age groups namely young (20–39 years of age), middle-aged (40–59 years) and elderly (60 years and above). We removed the antibiotic-treated subjects from all subsequent analyses. Principal component analysis of the microbiome profiles of the samples belonging to the three age-groups was performed and plotted using dudi.pco and s.class function of the ade4 R package. The significance of the association was obtained using the PERMANOVA (adonis function) implemented in the ‘vegan’ R package (with ‘country’ and the DNA extraction method as a confounder).

Grouping samples into country-/continent-specific bins

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Given that regional factors had the highest effect on the microbiome composition, it was important to ensure regional homogeneity for comparative disease-association analysis. However, the majority of disease-specific cohorts either displayed significant differences in age difference in the age of the control and diseased individuals (i.e. they were not age matched) or biases for disease patients from specific age-groups (Figure 2). For each disease, collating samples from the same country or continent-level as the disease cohorts would bypass the issue of limited sample numbers across the age-groups, whilst maintaining regional homogeneity of the cohorts (Figure 1—figure supplement 3A–B). To compare the overall effects of the two regional factors, country and continent, on the microbiome profiles we performed bootstrapped PERMANOVAs (by taking 20% subsets) within the control individuals. The results indicated that, although continent was observed to have a marginally lower effect on the microbiome composition compared to nationality (country), performing repeated bootstrapped comparisons indicated the effect of continent to have a higher significance (calculated as -log of Adonis P-values) than the country on the microbiome profiles (Figure 1—figure supplement 3C). For each disease, the country/continent specific affiliations of the disease cohorts were first obtained. Subsequently, we performed all the investigations pertaining to each disease by pooling samples belonging to the same country as the corresponding disease cohorts (referring to them as disease-specific country-level bins) (Figure 1—figure supplement 3D). This was expected to optimally homogenize the region-specific variations, while ensuring enough representation of various diseases (and controls) across age-groups. Wherever applicable, we have adopted a similar disease-specific regional grouping strategy at the level of continents.

Investigating the interaction between disease signatures and age-group

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For each disease, we performed PERMANOVA within the corresponding disease-specific country bins investigating the effect of the interaction of Disease and Age-group (Disease:Age-group) after adjusting for the effects of Country, and the independent effects of Disease and Age-group. Briefly, the pseudocode of the formula is provided below: adonis(species ~ country + disease + age-group + disease:age-group).

Disease classification using Random Forest (RF) Models

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If diseases have age-group specific signatures, then classifiers trained on the same age-group would have significantly better performance when tested on the same age-group as compared to that when tested on different age-groups. To evaluate the performance of disease classifiers trained on one age-group on disease prediction in either the same (Same Age-group classification) or different age-groups (Different Age-group classification), we adopted the following strategy (Figure 2—figure supplement 1). For each disease-age group combination, we performed 100 iterations, such that in each iteration, we trained the classifier on a subset of disease and the same number of control samples (50% of the minimum number of diseased samples across any age-group; denoted as ‘training subset’, the disease-specific training subset sizes as defined in Figure 2—source data 1). The evaluation of each of these disease classifiers for ‘Same Age-group’ and ‘Different Age-group’ classification was then performed using two approaches as mentioned below.

In the first approach, we created two ‘re-sampled’ test sets. While one contained Y diseased and Y control individuals from the same age-group (but not included in the training sub-set) (Same Age-group test set), the other contained Y diseased and Y control individuals from the other two age-groups (Different Age-group test set) (with Y defined as in Figure 2—source data 1). We tested the classifier on each test set and computed the AUCs. Testing the same classifier on both test sets ensured that the observed variations in disease prediction performance was not due to differences in the subject sub-samples used to create the classification models. Further to ensure that we don’t have biases introduced because of the selection of test sets (same age-group, different age-group), we repeat above steps 20 times (per classifier in each iteration) and computed the median AUCs for both the Same Age-group classification and the Different age-group classification. These median AUCs obtained for the Same Age-group and Different age-group classification for each of the 100 iterative sub-sampled classifiers was compared using Wilcoxon Signed Rank tests (to check if the performance of the classifiers significantly varied when tested on the same or different age-groups).

In the second approach, using Permutation test framework, we tested whether observed difference in classification AUCs (i.e AUC for Same Age-group classification – AUC for Different Age-group classification) was significantly different than what would be expected by random. For this, we needed null distribution of empirical differences of AUC for the same sub-sampled classifier. For this purpose, for each of the 20 iterations corresponding to each of the 100 sub-sample based RF classifier models (as described in the previous paragraph), we first merged the two ‘re-sampled’ test sets (Same Age-group test set and Different Age-group test set), then permuted the age-group labels of the subjects, creating two ‘Permuted’ test tests (Permuted test set 1 and 2), tested the classifier model for each of the Permuted test sets and finally computed the AUC differences obtained for the same classifier between the two permuted test sets. For each of the RF classifiers (across the 100 iterations), we computed the medians of the differences of AUCs (for the two permuted test sets) across the 20 iterations. The median difference of the AUCs obtained for the actual test (for Same Age-group – Different Age-group) and the permuted tests (Permuted Test set 1 – Permuted test set 2) obtained for the 100 iterations are then compared with Wilcoxon signed rank-tests. The objective of these permutation tests was to reduce the effects of correlated errors associated with certain ‘aberrant’ samples in influencing the disease prediction performance of RF classifiers for specific age-groups. This permutation test is expected at least ensure that the correlations introduced by sampling and by testing the same classifier on multiple cohorts will also be present in the null distributions. For both the approaches, the p-values of comparison across age-groups were corrected using Holms’ correction.

For a given disease, to ensure that the observed changes were not artefactual consequences of differences in sizes of training and testing subsets, we kept the training and testing subset sizes constant across all training age-groups. The classification AUCs, Specificities and Sensitivities were computed using the various modules in the pROC package.

Identifying the top disease-associated marker taxa for the different age-groups

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For each disease-age group scenario (as described above), we ranked the species-level taxa in decreasing order of their mean importance scores (mean decrease in GINI) across all 100 iterations. The iterative sub-sampling based Random Forest analysis avoided overfitting and, by virtue of the uniformity of training and test sizes for each disease across all age-groups, ensured that the differences across age-groups were not a simple consequence of differences in the number of diseased and control individuals. The objective of the iterative approach was also to identify taxa that showed stable association with disease irrespective of the sub-sampling population. This aimed to identify taxa that were always considered for the classification model irrespective of the sampled population of diseased and controls (Mean Decrease of GINI of at least 0 across at least 95% of iterations). So, for each disease/age-group scenario, we aimed to identify that minimum percentile threshold above which the taxa were considered for the classification model across at least 95% of the iterations (Figure 3—figure supplement 1A). We observed that for taxa, having a percentile score of at least 85, included in training models across at least 95% of the iterations across all the 13 disease age-group scenarios. Further plotting the mean feature scores of taxa arranged in terms of their percentile scores, we observed that the mean feature importance scores remained stable and low till the 80-percentile score and started increasing considerably only after that (Figure 3—figure supplement 1B). Based on these two results, the taxa having importance scores in the top 15 percentile (that is higher than 85 percentile) were identified as the top 85 percentile predictors/markers for a given disease in that age-group.

Validation of the shortlisted disease-associated taxonomic markers using linear models and identifying overlaps with taxa reported in original studies

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In this linear model based validation strategy, within the disease-specific country-level bins, we modelled the (log transformed to the base 10) abundance of each species first as a function of disease-status and country and then as a function of the interaction of disease-status and age-group and country as an independent predictor as described below:

Model 1:

Log(Species)~Country + Disease + Age-group

Model 2:

Log(Species)~Country + Disease * Age-group

(which is equivalent to Log(Species)~Country + Disease + Age-group + Disease:Age-group)

The goodness of fit of both models were quantified using adjusted R-squares and AIC values, and the significance of improvement of Model two with respect to Model 1, was judged using log-likelihood tests. This took care of the country as a confounder as well as provided the extent to which the interaction of disease and age-group had an influence on the abundance of the species as compared to the individual factors. Those species showing significant improvement in performance of Model two with respect to Model 1 (log likelihood ratio test p<0.05 one-sided), could be identified as ones for which age-group had significantly high influence on the disease associations. For the case of T2D, however, there were skews in the representation of the diseased samples across countries, where in the diseased samples in the young and middle aged group were only from the Chinese cohort, while those from the Elderly individuals had representation from both the Chinese and Swedish cohort. Consequently, to ensure regional homogeneity, in the above validation for T2D, we included young/middle-aged controls from only the Chinese cohort, while the elderly controls included those from both the Chinese and the European (Swedish for country-specific comparisons) cohorts. Taxa having significant differences in their RF feature importance scores (FDR < 0.01 at least one pair of age-groups using Dunns’ test for IBD, T2D and Cirrhosis and Mann-Whitney Tests for CRC and Polyps), which were also validated as having log likelihood ratio test p<0.05 in the linear model based validation were identified as the final validated list of age-group specific markers. This was done because the validation of each taxa was independent and additional stringent thresholds during validation would result in loss of sensitivity.

In order to evaluate the prevalence of these age-linked markers in the known disease-microbiome associations, for four of the five diseases, we obtained the list of associated species (detected as either significantly different or as a marker with high disease predictive score) from the original published studies corresponding to each of the disease cohorts (Feng et al., 2015; Franzosa et al., 2019; Karlsson et al., 2013; Qin et al., 2012; Qin et al., 2014; Vogtmann et al., 2016; Zeller et al., 2014) (Figure 3—source data 3). For CRC, we utilized the list of markers provided in Thomas et al. (2019). We specifically utilized the list from this study, as it had already performed a multi-cohort meta-analyses of CRC-specific taxa adopting a similar metaphlan2-humann2 based classification scheme. The list (as created above) was then compared with list of age-linked markers specific to each disease (Figure 3—source data 3).

Reproducing the results of CRC-specific trends on the validation cohorts

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Iterative age-group specific disease classifiers were devised and tested as described in Figure 2—figure supplement 1 (by specifically setting the ‘Training’ and ‘Testing’ cohorts). For classifiers designed on the validation cohort, the feature score importance scores of the top 85 percentile markers (obtained during the iterations) were compared using Mann-Whitney U-tests between each age-group (in each cohort). For testing the reproducibility of the associations, the directionality of the 19 known markers and the significance of their differences in the two training cohorts were then obtained and compared. For investigating the stability of the microbiome signatures (obtained for the different age-groups) across training cohorts, we profiled the mutual variations in the feature rank profiles obtained in cohort with respect to those obtained for the other. For a given feature rank profile (obtained in a given cohort for a specific age-group), its mean Spearman distance to all the feature rank profiles obtained for the same age-group in the other cohort was obtained. This was then collated for all feature profiles for the same age-group in both the cohorts. The distribution of the distances would indicate the stability of the microbiome signature across cohorts. Lower cross-cohort distances would indicate stable reproducible signatures and higher cross-cohort distances signify a relative lack of signature.

The ageing associated changes in the prevalence of some of the CRC markers and their effect relative variation within disease and controls were profiled in the following manner. In the first step, we compared the prevalence rates of these markers in the elderly controls and the young-middle aged controls using Fishers’ Exact tests separately for the two cohorts. The p-values obtained for each cohort were then merged using the Fisher method. The effect size difference (disease v/s control) distribution for the markers computed using age-group specific iterations wherein each iteration, we compared 20 disease samples with 20 controls and computed the effect size using Cohens’ D. Empirically, a Cohens’ D values of less than 0.2 indicates low effect and greater than 0.2 indicates medium to high effect.

Determining the directionality of disease association in each disease-age group scenario

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To obtain the directionality (increased or decreased in disease) of the of the microbiome changes, Mann-Whitney U tests were first performed for all taxa to compare their abundances in ‘study-matched’ control and diseased samples (for ensuring regional homogeneity) from the specific age-group (ref to Figure 1C for the Study cohorts). The taxa having significant differences (Mann Whitney Test p<0.05) in their abundances between control and diseased samples were identified along with their directionality (ie. increased in disease or decreased in disease). These analyses were finally re-performed at the level of continent (that is between ‘continent-matched’ disease and control samples) to ensure statistical power to detect the gain versus loss patterns. For each disease age-group scenario, the top 85 percentile markers having significant change in their abundance with FDR corrected p-values<0.01 (for the continent level comparisons) were then filtered (Figure 5—source data 1). The directionality of these markers was assigned based on the trends of their abundance patterns, as either 'Increased' or 'Decreased' in disease.

Creation of generic disease prediction models and identification of shared disease markers

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The generic disease prediction classifiers were developed in a manner similar to that shown in Figure 1—figure supplement 3. The only difference was the agglomeration of equal number (n = 10) samples from each of the diseases (rather those of a specific disease) as described below. For each age-group (young/middle-aged or elderly), a generic disease cohort was created by taking equally sized sub-samples from each disease (to remove biases in the classifiers originating from specific diseases). The sub-sample sizes were also kept the same across the age-groups to ensure uniformity in the testing and training sizes of the classifiers across all age-groups. The iteration was subsequently repeated five times using a different (but equally sized) subset of diseased and control samples (as described above). The AUC and sensitivities for the five repetitions were then merged and compared across the young/middle-aged and elderly. To remove regional biases in microbiome compositions affecting these results, all analyses were restricted within the disease-specific continent cohorts (Figure 1—figure supplement 3D).

Identification of Frailty-associated markers

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We used a random forest model to regress both the Functional Independence Measure (FIM) and the Barthel Score (both an inverse measure of frailty) of an individual from the microbiome profile. Random forest regression training was performed on 20% of the samples and tested on the remaining 80%. The ranked feature importance scores of the different species were then obtained. Microbiome features (that is the species) were ranked in decreasing order of their feature importance scores (mean decrease in GINI coefficient upon excluding the feature). The mean feature ranks for the different groups of species (G1-L3) were then calculated.

To identify the most predictive marker set, the regression was repeated by iteratively reducing the number of the top microbiome features, and the mean error was calculated for each iteration. For both FIM and Barthel Score, the number of top microbiome features for which the error was minimum was taken as the set of the top frailty-associated markers. For both FIM and Barthel Score, the minimum number of features were 8 and 6, respectively.

The median FIM values were computed for both the residential care cohort and the overall cohort (community + residential care). For each cohort, individuals having FIM values below and above the corresponding median were classified as ‘Frail’ and ‘Non-Frail’, respectively. To validate the metabolite signatures, measured levels of actual metabolites, abundances specific microbial pathways and gene-families (obtained using humann2) were then either compared between these groups of ‘Frail’ and ‘Non-Frail’ individuals or correlated with the FIM values.

Creating metabolite species maps and obtaining the frailty-associated metabolic signature of a given group of species

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We utilized the literature-curated experimentally annotated species to metabolite (production/consumption) associations available as part of the Virtual Metabolic Human database as well as those obtained in a recent meta-analysis by Noronha et al. (2018); Sung et al. (2017), to create a species-to-metabolite map of more than 300 metabolite production and consumption profile corresponding to 992 species in a 0 (absent) and 1 (present) notation (Figure 6—source data 2). For each microbiome, the metabolite production/consumption capability was then obtained as the matrix inner product of the abundance profile of the species and the species-to-metabolite map thus obtained.

Next, we identified the frailty-linked metabolites associated with the eight taxonomic markers of frailty using a two-step strategy. First, we performed a correlation analysis of each metabolite profile (i.e the cumulated abundance of taxa previously associated in literature with a given metabolic capability as obtained above) with FIM scores and identified metabolite profiles that showed significant association with FIM scores (Spearman Rho; FDR of less than 0.25). Next, we identified which of these identified metabolite profiles were detected in the taxonomic markers of frailty (based on previous literature) at a rate significantly higher than the background detection (using our Fishers’ exact test approach with FDR corrected p<0.25.

Data and code availability statement

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The detailed description of the codes is provided in the methods section. The key in-house source codes used in this meta-analysis have been provided as Supplementary file 4. The shotgun data of the ELDERMET is available for download from the ELDERMET website at http://eldermet.ucc.ie/temp1/eldermet_shotgun_data_filtered_all_sample.tar. The shotgun data for the ELDERMET has also been uploaded at the European Nucleotide Archive (ENA) with the project accession number PRJEB37017.

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Decision letter

  1. Peter Turnbaugh
    Reviewing Editor; University of California, San Francisco, United States
  2. Wendy S Garrett
    Senior Editor; Harvard T.H. Chan School of Public Health, United States
  3. Catherine A Lozupone
    Reviewer; University of Colorado Denver - Anschutz Medical Campus, United States
  4. Peter Turnbaugh
    Reviewer; University of California, San Francisco, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This work emphasizes the importance of controlling for age in microbiome research and provides strategies for adjusting for this confounding factor across multiple disease areas. These results also set the stage for follow-on studies of the mechanisms responsible for these associations and their relevance to health and disease.

Decision letter after peer review:

Thank you for submitting your article "Adjusting for age improves identification of gut microbiome alterations in multiple diseases" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Peter Turnbaugh as the Reviewing Editor and Reviewer #2, and the evaluation has been overseen by Wendy Garrett as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Catherine A Lozupone (Reviewer #1).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

In this paper, the authors perform a meta-analysis of publicly available shotgun metagenomics datasets and identify interactions between age and microbiome associates with different diseases. Overall, this work addresses an important question. In addition to yielding insights into microbiome correlates across a variety of diseases when controlling for age in this particular meta-analysis, it provides guidance to other researchers that age is an important component to consider in study design when conducting microbiome/disease research. The manuscript is well written and state-of-the-art analysis techniques are applied. The overall conclusions are interesting in that random forest classifiers in the elderly group do not do a good job classifying the young (and vice versa), and that there are shared microbiome signatures with diseases that are affected by age.

Essential revisions:

1) The evaluation relies on random forests, a tool for classification, when the paper is really more about adjusting for confounders, which RFs don't intrinsically handle. The authors might be better served using an established meta-analytic framework like a mixed effects model. In order to use the RF for their purposes, the authors have to subsample their data extensively and have to perform a lot of pairwise analyses where controls from the same continent are pooled (and differences between countries on the same continent ignored). A hierarchical model should be able to capture and adjust for effects of continent and country in a way that made more efficient use of the available samples, would offer some protection against unbalanced examples, and would have more easily interpretable coefficients. While it's true that random forests have better classifier performance than, e.g. a logistic regression, the resulting models are harder to interpret, and because the methods here are somewhat ad hoc, it is tough to get a sense for how the decisions the authors made in processing the data ended up affecting the results.

1a) In particular, the analyses involve a lot of comparisons of heavily derived summary statistics from random forest models, like AUC and feature importance, but the statistical significance of these differences is not always clear. While Figure 1 does appear to use a statistical test to evaluate AUC differences (for models trained on one age group and evaluated on the same/different age groups), the replication for CRC in Figure 3A does not. I also can't find a description of what test was used and what cutoff was called significant.

1b) The authors also say that the pattern they observe in Figure 3A is "exactly the same" as in Figure 1D, but it is difficult to tell whether this is accurate. In Figure 1D, performance mainly drops for models tested on elderly (E) samples, whereas in Figure 3A, performance mainly drops for models trained on (E) samples, with very little difference in evaluation performance when models trained on young/middle (YM) samples are evaluated on E samples. In fact, the AUCs in the lower panel of Figure 3A are all pretty similar, despite the strong color differences – a more globally consistent color scheme would help, and speaking of color, there seems to be an error in Figure 1D – Cirrhosis (0.97 is shaded darker than 0.95).

1c) In Figure 2, the cutoff (eighty fifth percentile of Gini decrease) is never justified and the subsequent filtering step struck me as unclear. The authors say they performed Mann Whitney U tests of marker scores across at least two age groups, but isn't there only one marker score per age bracket?

2) The authors mainly graph derived measures like AUC and feature importance without ever really visualizing the original data, with the exception of some of the metabolite measurements in the last figure. The closest we get are the name colors in Figure 2. This is important to sanity-check the results based on summary statistics, to show more clearly the extent of the age confounding, and to show what it is specifically about the aging-related markers that is being captured by the model. Visualization could capture whether, for example, in addition to greater loss, E samples had more "noise" or β-diversity compared to YM samples, and whether the aging signal was consistent across continents or driven by different taxa in different locations. Without visualizing, it's hard to get a sense of the scope of the issue.

3) The authors were sensitive to the fact that DNA sequencing/ extraction methodology can affect microbiome profiles which is good. They handled this by excluded some samples from a particular DNA extraction methodology that produced differences in observed diversity. It is strange, however, that after this step to reduce this confounding, addition samples from studies of IBD, CRC and ELDERMET were then added. Why would one add new samples after and not before taking steps to address effects of DNA sequencing/ extraction methodology? Later in the paper it becomes clearer that these other added studies are more validation cohorts and perhaps this is why? This should be clearer. Also, since there still was a minor effect of these factors after removing the samples with a large DNA extraction effect, it might be good to adjust for these differences in methods in the Adonis tests (e.g. DNA extraction + age in the model). Also, is seems that other factors about experimental protocol could also affect observed diversity- e.g. which kit was used to prepare the libraries (Tru-Seq versus others)?

4) It is unclear how the Adonis/PERMANOVA was performed to produce the data show in in Figure 1A. Our impression is that these tests were run independently for each metadata category but that is not completely clear. It seems that taking advantage of being able to control for confounding by running Adonis with more complex models (as suggested above for DNA extraction) might be good.

5) We'd like to see the data presentation and interpretation modified to try to avoid a potential bias towards age as an important covariate. Multiple metrics presented throughout the study are equivocal or even supportive of consistent trends despite age. The authors should be careful to point this out and to present a more nuanced interpretation that only some aspects of the microbiome are associated with aging.

6) The authors need to revise the main text to be more careful about the language used to avoid conflating correlation with causation. Words like "impact", "reinforced by", "influence", etc. imply that there is causal information where none exists.

7) The full dataset analyzed here needs to be made publicly available prior to publication. It's not sufficient to ask readers to re-assemble the full meta-analysis on their own.

8) Add analyses wherein each individual study is analyzed separately, as opposed to the current analysis which involves merging data across all the studies. The current approach has the downside of being influenced by the larger studies, whereas the former would allow for statements about the degree to which associations with age are reproducible across studies. That said, I'm not sure this is possible given the nature of the data. At a minimum, it would help to have some supplemental figures that depict the distribution of ages within and across studies.

9) The Discussion section really nails a critical confounder, drug use, which could explain a lot of what is described here. Diet and lifestyle are also likely contributors to these associations. While I don't think these data can be used to de-couple these factors, it would be good to be more explicit about this issue in the abstract and introduction. As written, the reader might take away the possibly false conclusion that aging itself (independent of diet, drugs, etc) is associated with the gut microbiome.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for submitting your article "Adjusting for age improves identification of gut microbiome alterations in multiple diseases" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Peter Turnbaugh as the Reviewing Editor and Reviewer #2, and the evaluation has been overseen by Wendy Garrett as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Catherine A Lozupone (Reviewer #1).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

The authors have expanded the analyses they originally put forth showing that age is a potential confounder in microbiome association studies. The addition of the PERMANOVA and the linear modeling makes the paper stronger, and the reviewers appreciated the authors making it clearer how they determined significance in the random forest studies.

Essential revisions:

Two remaining concerns about the statistic used need to be addressed prior to publication.

1) We appreciate seeing the distributions of AUCs that the authors generated, and this does help to make their point more convincing. However, now that we understand better what they did, we're not sure that the test they used is appropriate. It seems that the authors are partitioning studies into age groups, training on a randomly-sampled subset of one age group, then testing on the remaining held-out samples from the 1-2 other age groups. This is all good practice so far. The issue is that the authors seem to be doing this repeatedly to get a distribution of AUCs per cohort, then testing for significant differences between the AUC distributions per cohort, using a Mann-Whitney U test. The problem with this is that AUCs within a cohort are not actually independent of each other (which the M-W test requires), because some classifiers will have been trained on the same subsamples. This could make the distribution of AUCs look narrower than it really is. Further, the same classifier is being used on each age group, which means there are also correlated errors across cohorts. Testing for a significant difference between AUCs is actually a pretty subtle and difficult problem, which is another reason we prefer making statements about significance using explicit statistical models like logistic regression, whose properties are better understood.

If the authors really want to test for significant differences between these models, we would encourage them instead to use a permutation test: i.e., shuffle the labels of the age cohorts repeatedly, then use the empirical distribution of differences in AUCs between cohorts to test for significance. This should at least ensure that the correlations introduced by sampling and by testing the same classifier on multiple cohorts will also be present in the null distributions.

2) We also have one more substantive concern with Figure 5. We appreciate the authors did not drop low nominal p-values, but it is also not appropriate to use a fold-change cutoff before applying p-value corrections. Any filtering that uses information about the difference between groups (which includes fold-change cutoffs) is statistical double-dipping and will tend to inflate significance. You can either apply the fold-change cutoff after p-value correction or apply a filter that doesn't depend on knowledge about which samples belong to which groups, like the overall variance or the number of non-zero observations.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Adjusting for age improves identification of gut microbiome alterations in multiple diseases" for further consideration by eLife. Your revised article has been evaluated by Wendy Garrett (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

It is unclear in the main text why 2 different approaches are needed – especially since the last paragraph of subsection “Influence of age on the microbiome and microbiome-disease signatures” only refers to the results of the permutation based strategy. Seems like might be more straightforward to just report the latter, but at the least the results of both and not just one of the approaches should be reported if both are described.

In subsection “Influence of age on the microbiome and microbiome-disease signatures”, where authors say "In summary, in each disease-age-group scenario, we used two different approaches. " They should qualify for what – "e.g. we used two different approaches to assess whether disease classification performance was significantly different between same age group classification and different age group classification".

In subsection “Influence of age on the microbiome and microbiome-disease signatures”: "is significantly different from what would be expected by random (null distribution) " should briefly quality "generated by permuting the age-group labels of the subjects"

There was also a minor concern with the permutation test. The authors seem to be getting one average true or permuted AUC for each of the 100 resampled classifiers, instead of averaging across classifiers and getting one AUC per permutation (comparing the "true" value to this null to get a p-value). Because of non-independence between the training and test samples the classifiers used, the author's procedure should yield narrower AUC distributions, which could inflate significance. A back of the envelope estimate suggests an average overlap between classifiers of around 10-20% of the training + test samples. Given the consistency of the story and the magnitude of the differences the authors observe, though, as well as the computational time cost of doing enough permutations to get an accurate p-value, we leave it up to the authors whether they want to 'inoculate themselves against' this particular criticism.

Several different FDR cutoffs are used (0.15, 0.20, 0.25) with no explanation. Figure 6D seems particularly arbitrary in its use of two different thresholds, neither of which is that common (0.15 and 0.20). For that figure, in the absence of a really good reason to pick those specific cutoffs, it would probably be more transparent to just pick one standard threshold (e.g. 0.25) and to just make it really clear that Figure 6D represents a selection of the most significant of the resulting hits. The authors would of course also need to report how many total hits were significant at this threshold, and to make the full list available.

The authors should pick either one of Spearman or Kendall distances; using both in different places is confusing. (Bray-Curtis is another option for calculating divergence between microbiome abundance profiles.)

https://doi.org/10.7554/eLife.50240.sa1

Author response

Essential revisions:

1) The evaluation relies on random forests, a tool for classification, when the paper is really more about adjusting for confounders, which RFs don't intrinsically handle. The authors might be better served using an established meta-analytic framework like a mixed effects model. In order to use the RF for their purposes, the authors have to subsample their data extensively and have to perform a lot of pairwise analyses where controls from the same continent are pooled (and differences between countries on the same continent ignored). A hierarchical model should be able to capture and adjust for effects of continent and country in a way that made more efficient use of the available samples, would offer some protection against unbalanced examples, and would have more easily interpretable coefficients. While it's true that random forests have better classifier performance than, e.g. a logistic regression, the resulting models are harder to interpret, and because the methods here are somewhat ad hoc, it is tough to get a sense for how the decisions the authors made in processing the data ended up affecting the results.

We are grateful to the reviewers for this suggestion and have we have now addressed this as described below.

1) First, to establish that age as a covariate influences disease signature, the additional PERMANOVA has been performed for each disease with a more complex approach modelling the gut microbiome as a function of the interaction of age-group and disease taking country as a confounder as described below:

adonis(Microbiome ~ Country + Disease*Age-group)

This computes the extent of age-group associated disease signatures for each age-group (Disease:Age-group term) after adjusting for the extent of influence that disease and age-group independently have on the microbiome. For four of the diseases, age-group associated disease signatures (Disease:Age-group) has a significant influence on the microbiome composition (even after stratification for country). The newly added methodology used for this purpose has been described in subsection “Creation of generic disease prediction models and identification of shared disease markers”. The new results obtained in this analysis has been described in subsection “Influence of age on the microbiome and microbiome-disease signatures”. We have added a new Table 1 to show the results of this analysis. Overall, interaction between two metadata types measures the extent to which the microbiome variations with respect to one metadata (in this case, the disease) is influenced by the variation in the other. For three of the diseases (IBD, CRC and Polyps), the effect of the interaction of disease with age-group was significant (all with P < 0.05; PERMANOVA). The effect was also marginally significant for T2D (P < 0.08; PERMANOVA). This indicated that (even after taking into account the regional variations and the individual influences of disease and age), for these diseases, the microbiome variation associated with disease was significantly impacted by the variations in the age-group of the individuals. Further investigation of these influences (measured by the R2) also indicated that the extent of these effects was disease-specific, whereby IBD and CRC had the highest influence of age as a covariate of disease signatures, with T2D and Polyps having considerably lower influences (Table 1).

2) The second objective of this study was to probe and identify the taxa showing age-specific associations with disease. This is synonymous to asking to what extent age as a covariate influences the abundance changes associated with each taxon in different diseases. This was investigated using a combination of two different analogous approaches, as described below.

The first approach involved identifying the disease-associated microbiome signatures for different age-groups and investigating whether these signatures obtained from one age-group are extrapolatable to other age-groups, and subsequently investigating the differentially associated features. This was the approach that we had used in the previously submitted version of the manuscript.

We used Random Forest based approach because of the following reason. Random Forests can not only be used to judge the strength of the association between the predictors and the response (based on the accuracy or area under the curve (AUC) of prediction), but also whether associations identified on one set of observations are extrapolatable to another. Consequently, Random Forest models have been routinely used in microbiome based studies to not only quantify and characterize the microbiome associations with various diseases (Feng et al., 2015; Karlsson et al., 2013; Pasolli et al., 2016; Qin et al., 2012), but also to study the transferability of the associations predicted in set of individuals on others (He et al., 2018; Thomas et al., 2019). We have clarified this in subsection “Influence of age on the microbiome and microbiome-disease signatures”.

For such an approach, Random Forest, besides providing classification models superior to other methods like logistic regression, also provides the additional benefit of identifying the optimal features involved in the classification (as feature importance scores). The iterative decision tree-based approach implemented in Random Forests facilitates identification of co-occurrence. Another advantage is that the microbiome features (as a whole) could be inputted and used to create the model (something that cannot be performed in regression-based models). Additionally, to avoid overfitting and to ensure that the differences across age-groups were not a simple consequence of differences in the number of diseased and control individuals, we used an iterative approach (based on sub-sampling) that ensured uniformity of training and test sizes for each disease across all age-groups and the changes in feature signatures are not due to presence of specific unbalanced outlier examples for certain disease-age-group scenario.

However, we agree with the reviewers that, despite its simplicity, the above approach has one major limitation. It cannot consider the effect of confounders like (in this case), the country. We tried to address this first by restricting the Random Forest analysis to country-level bins (rather than continent-level bins as used in the previous submission) specific for each disease. Given that country had the highest effect on the gut microbiome, this was expected to address the regional confounding effect even more as compared to the previous version of the manuscript (as all comparisons are performed considering only those samples belonging to the same country as disease cohorts). The number of diseased and control samples obtained after this grouping has been summarized in a newly added Figure 1—figure supplement 3A. This grouping was not something we had explored in the initial version). This is described in subsection “Influence of age on the microbiome and microbiome-disease signatures”. By collating samples from only those datasets obtained from the same countries as the disease cohorts, we ensured that each disease-specific bin was regionally homogenous in terms of membership (thereby addressing the reviewer’s concern).

However, the above approach still does not capture one aspect. Microbiome associations with disease have both an age-group-specific and an age-group-independent component. However, many of the age-group-specific changes in microbiome-disease association may also be reflections of changes that accompany ageing in general. Age had the second major effect on microbiome composition, and it was important to investigate these age-specific changes in disease-microbiome associations after deconvoluting for the effect of ageing. Secondly, while grouping samples into country-level bins ensured enough regional homogeneity of the compared groups, there were still biases in the representation of certain regions in certain age-groups for the different diseases (Figure 1—figure supplement 3). Given that RF models cannot intrinsically adjust for these confounding effects, we probed this using a hierarchical linear regression-based strategy that compared the extent of influence that age-group had on the disease-association pattern of a taxon (Disease:Age-group) as described below.

In this linear model based validation strategy, within the disease-specific country-level bins, we modelled the (log transformed to the base 10) abundance of each species first as a function of disease-status and country and then as a function of the interaction of disease-status and age-group and country as an independent predictor as described below:

Model 1

Log(Taxon) ~ Country + Disease + Age-group

Model 2

Log(Taxon) ~ Country + Disease * Age-group, which is equivalent to:

Log(Taxon) ~ Country + Disease + Age-group + Disease:Age-group

The goodness of fit of both models was quantified using adjusted R-squares and AIC values, and the significance of improvement of Model 2 with respect to Model 1, was judged using one-sided log-likelihood tests. This took care of the country as a confounder as well as provided the extent to which the interaction of disease and age-group had an impact on the abundance of the species as compared to the individual factors.

We specifically investigated those taxa having significant differences in their feature importance scores (Figure 2—source data 1) and identified those having significantly higher influence of Disease interacting with Age-group than Age-group alone (Log likelihood test one sided P < 0.05) (Figure 2—source data 2; Figure 2B) as the final validated list of ‘strictly age-specific disease markers’. The results of this analysis have been described in subsection “Age-centric differences in microbiome-disease associations”. The methodology has been described in subsection “Validation of the shortlisted disease-associated taxonomic markers using Linear Models and identifying overlaps with taxa reported in original studies”.

3) The third key aspect of the current study was the gain versus loss patterns in disease association across age-groups. For this, we first performed the analyses within cohorts (now summarized in the new Figure 1C). We observed that for most of the diseases, the pattern obtained was like the one obtained earlier that is increase of taxa that are lost in disease in the elderly as compared to the young age-groups (newly added Figure 4—figure supplement 1). However, because of lower sample sizes and lower power, we had to perform the comparisons at relaxed thresholds of Mann-Whitney Test P-value < 0.05. Therefore, to increase statistical power and validate the above trends on a much larger cohort we repeated the analysis on a much larger continent-level bins and reproduce this pattern which was already observed in Figure 4A (unchanged in the current version). In this comparison, the features were identified with Benjamini Hochberg corrected Mann Whitney P < 0.1. These results have now been described in subsection “Reproducible association of the G1 disease-positive markers with increased frailty in elderly individuals from the ELDERMET cohort”.

In summary, in the current version of the manuscript, although cohort specific biases still exist, we have used a combination of Random Forest, linear models as well as validations at different levels of regional homogeneity to enhance the reliability of results as well as remove convoluting effect of general ageing on these age-specific disease signatures. We have described this in the Discussion section.

1a) In particular, the analyses involve a lot of comparisons of heavily derived summary statistics from random forest models, like AUC and feature importance, but the statistical significance of these differences is not always clear. While Figure 1 does appear to use a statistical test to evaluate AUC differences (for models trained on one age group and evaluated on the same/different age groups), the replication for CRC in Figure 3A does not. I also can't find a description of what test was used and what cutoff was called significant.

For both Figure 2A (earlier Figure 1D) and 3A, we have used Mann-Whitney U tests to compare the AUC distributions for the 100 Random Forest iterations obtained for the various scenario. Instead of tables, Figure 2A now shows the boxplot of the distribution of disease classification AUCs of classifiers trained on either the same or different age-groups. We feel that this clearly shows the differences in the classification performances across age-groups, and removes the confusion associated with the colours and values as earlier. We have now described this in the revised manuscript (subsection “Age-centric differences in microbiome-disease associations”; subsection “Age-specific changes in the directionality of taxon abundance alterations for specific diseases, and the microbiome response shared by multiple diseases”).

1b) The authors also say that the pattern they observe in Figure 3A is "exactly the same" as in Figure 1D, but it is difficult to tell whether this is accurate. In Figure 1D, performance mainly drops for models tested on elderly (E) samples, whereas in Figure 3A, performance mainly drops for models trained on (E) samples, with very little difference in evaluation performance when models trained on young/middle (YM) samples are evaluated on E samples. In fact, the AUCs in the lower panel of Figure 3A are all pretty similar, despite the strong color differences – a more globally consistent color scheme would help, and speaking of color, there seems to be an error in Figure 1D – Cirrhosis (0.97 is shaded darker than 0.95).

We apologize for this lack of precision in our language. Our intention was to convey that the pattern observed in Figure 3A was similar to that observed in the earlier Figure 1D (now Figure 2A) for CRC, where in the highest AUC was observed for classifiers trained on the young and the middle aged group and tested on individuals belonging to the same age-group, with a significant loss of performance for those either trained or tested on the elderly individuals. This indicates a loss of microbiome associated disease signature for CRC in the elderly. To avoid confusion arising out of colour differences in heatmaps showing median values of AUC (in both the earlier Figure 1D and Figure 3A), we have now provided boxplots that show the distribution as well as the comparison of AUC values using Mann-Whitney Tests. We have also improved the language used to describe the similarity of the age/AUC correlations (subsection “Age-specific changes in the directionality of taxon abundance alterations for specific diseases, and the microbiome response shared by multiple diseases”).

1c) In Figure 2, the cutoff (eighty fifth percentile of Gini decrease) is never justified and the subsequent filtering step struck me as unclear. The authors say they performed Mann Whitney U tests of marker scores across at least two age groups, but isn't there only one marker score per age bracket?

To clarify this reviewer comment, a short explanation of how we arrived at this eighty fifth percentile threshold has now been added to the revised version of the manuscript (Materials and methods section) along with an additional Figure 2—figure supplement 1. The explanation is summarized below:

The iterative sub-sampling based Random Forest analysis avoided overfitting and, by virtue of the uniformity of training and test sizes for each disease across all age-groups, ensured that the differences across age-groups were not a simple consequence of differences in the number of diseased and control individuals. The objective of the iterative approach was also to identify taxa that showed stable association with disease irrespective of the sub-sampling population. This aimed to identify taxa that were always considered for the classification model irrespective of the sampled population of diseased and controls (Mean Decrease of GINI of at least 0 across at least 95% of iterations). So, for each disease/age-group scenario, we aimed to identify that minimum percentile threshold above which the taxa were considered for the classification model across at least 95% of the iterations (Figure 2—figure supplement 1A). We observed that for taxa, having a percentile score of at least 85, included in training models across at least 95% of the iterations across all the 13 disease age-group scenarios. Further plotting the mean feature scores of taxa arranged in terms of their percentile scores, we observed that the mean feature importance scores remained stable and low till the eightieth percentile mark and started increasing considerably only after that (Figure 2—figure supplement 1B). Based on these two results, the taxa having importance scores in the top 15 percentile (that is higher than eighty fifth percentile) were identified as the top eighty fifth percentile predictors/markers for a given disease in that age-group.

2) The authors mainly graph derived measures like AUC and feature importance without ever really visualizing the original data, with the exception of some of the metabolite measurements in the last figure. The closest we get are the name colors in Figure 2. This is important to sanity-check the results based on summary statistics, to show more clearly the extent of the age confounding, and to show what it is specifically about the aging-related markers that is being captured by the model. Visualization could capture whether, for example, in addition to greater loss, E samples had more "noise" or β-diversity compared to YM samples, and whether the aging signal was consistent across continents or driven by different taxa in different locations. Without visualizing, it's hard to get a sense of the scope of the issue.

We thank the reviewers for pointing this out. As described above in responding to previous comments, we have now performed additional analysis in the current version of the manuscript to address these concerns. To identify the extent of age-confounding, using PERMANOVA analysis, we first evaluate and establish whether and to extent age as a covariate influences taking into account the country-specific variations in microbiome signatures as described below:

adonis(Microbiome ~ Country + Disease*Age-group)

This computes the extent of influence that disease and age-group independently have on the microbiome as well as the extent of age-group associated disease signatures for each age-group (Disease:Age-group term). For four of the diseases, age-group associated disease signatures (Disease:Age-group) has a significant influence on the microbiome composition (even after stratification for country). The newly added methodology used for this purpose has been described in subsection “Creation of generic disease prediction models and identification of shared disease markers”. The new results obtained in this analysis has been described in subsection “Influence of age on the microbiome and microbiome-disease signatures”. We have added a new Table 1 to show the results of this analysis.

Regarding the second point of the reviewer regarding the nature of ageing related markers that were being identified by this approach. Age-group specific markers can be classified in two types. The first type includes those that show differential associations with disease, because their abundance changes with increasing age (even in the controls). The second type includes those which show significantly different associations with disease across age-groups even after accounting for the changes that happen with ageing. In the revised version of the manuscript, we have used a combination of both approaches, namely Random Forest and Linear Modelling to understand these effects. RF would identify both types. the modelling would identify the second. Consequently, we first use Random Forest models to assess the performance of disease prediction models devised on one age-group on others as well as to identify a filtered optimal list of age-group specific markers (as earlier). Subsequently, we validate the age-specific disease association trends of each, filtered using linear models adjusting for the effect of country-specific variations. In this validation approach, we model the (centralized log ratio transformed) abundance of each species, first as a function of disease-status and country, and then as a function of the interaction of disease-status and age-group and country as an independent predictor, as described below:

Model 1

Log(Species) ~ Country + Disease + Age-group

Model 2:

Log(Species) ~ Country + Disease * Age-group

The goodness of fit of both models was quantified using adjusted R-squares and AIC values, and the significance of improvement of Model 2 with respect to Model 1, was judged using log-likelihood tests. This took care of the country as a confounder as well as provided the extent to which the interaction of disease and age-group had an impact on the abundance of the species as compared to the individual factors.

We specifically investigated those taxa having significant differences in their feature importance scores (Figure 2—source data 1) and identified those having significantly higher influence of Disease interacting with Age-group than Age-group alone (Log likelihood test one sided P < 0.05) (Figure 2—source data 2; Figure 2B) as the final validated list of ‘strictly age-specific disease markers’. The results of this analysis have been described in subsection “Age-centric differences in microbiome-disease associations”. The methodology has been described in subsection “Validation of the shortlisted disease-associated taxonomic markers using Linear Models and identifying overlaps with taxa reported in original studies”.

Furthermore, in line with the reviewers’ suggestion, we also checked whether the samples from elderly controls had significantly higher variability as compared to young/middle-aged controls, across the different continental regions (Figure 4—figure supplement 2). In line with our hypothesis, for both Europe and North America, gut microbiome from elderly individuals were significantly more variable as compared to young and middle-aged controls. This was however not observed in the Asia (Chinese) cohort. Interestingly, Cirrhosis which neither shows neither an association with disease signatures nor the gain versus loss pattern contained only subjects from Asia (summarized in subsection “Reproducible association of the G1 disease-positive markers with increased frailty in elderly individuals from the ELDERMET cohort”). This indicates that ageing has different patterns across continents. This increasing loss could lead to dysbiotic configurations characterized by higher inter individual variability, increased abundance of pathobionts (resulting in loss of disease signature) thereby making the microbiome more susceptible to diseases.

However, due to skewness of the age distribution across datasets, validations with respect to the reproducibility of the disease signatures could not be performed for all diseases. However, for CRC (which was observed to have one of the strongest influences of age on disease signatures), we had additional validation cohorts and we have tested these aspects extensively in the validation cohorts (Figure 3; subsection “Age-specific changes in the directionality of taxon abundance alterations for specific diseases, and the microbiome response shared by multiple diseases”).

3) The authors were sensitive to the fact that DNA sequencing/ extraction methodology can affect microbiome profiles which is good. They handled this by excluded some samples from a particular DNA extraction methodology that produced differences in observed diversity. It is strange, however, that after this step to reduce this confounding, addition samples from studies of IBD, CRC and ELDERMET were then added. Why would one add new samples after and not before taking steps to address effects of DNA sequencing/ extraction methodology? Later in the paper it becomes clearer that these other added studies are more validation cohorts and perhaps this is why? This should be clearer. Also, since there still was a minor effect of these factors after removing the samples with a large DNA extraction effect, it might be good to adjust for these differences in methods in the Adonis tests (e.g. DNA extraction + age in the model). Also, is seems that other factors about experimental protocol could also affect observed diversity- e.g. which kit was used to prepare the libraries (Tru-Seq versus others)?

As pointed out by the reviewer, the additional samples were added as validation cohorts. We have clarified this in subsection “Influence of age on the microbiome and microbiome-disease signatures”. We also appreciate the reviewers’ suggestion to include the DNA extraction method as a factor and have included this in the computation of the PERMANOVA values in Figure 1A,B of the current version of the manuscript, as noted in subsection “Influence of age on the microbiome and microbiome-disease signatures” (described also in Materials and methods section).

4) It is unclear how the Adonis/PERMANOVA was performed to produce the data show in in Figure 1A. Our impression is that these tests were run independently for each metadata category but that is not completely clear. It seems that taking advantage of being able to control for confounding by running Adonis with more complex models (as suggested above for DNA extraction) might be good.

As suggested by the reviewer, we have now included the DNA extraction method as a factor in the PERMANOVA analysis for Figure 1A, and briefly mentioned this in subsection “Influence of age on the microbiome and microbiome-disease signatures” and in the Materials and methods section. However, we would like to respectfully point out that the objective of this analysis was to independently assess the importance of each of the factors on the microbiome composition and not to add complexity to the model. This analysis provided us the relative importance of each metadata on the microbiome composition, based on which we have addressed the appropriate confounders in the subsequent analyses.

5) We'd like to see the data presentation and interpretation modified to try to avoid a potential bias towards age as an important covariate. Multiple metrics presented throughout the study are equivocal or even supportive of consistent trends despite age. The authors should be careful to point this out and to present a more nuanced interpretation that only some aspects of the microbiome are associated with aging.

We appreciate the reviewer’s suggestion. The PERMANOVA analysis performed in the current version precisely addresses this. Age as a covariate of disease signature has a significant but reduced effect on microbiome composition. The extent of these associations is also disease specific. We have explained this in subsection “Influence of age on the microbiome and microbiome-disease signatures” and the Materials and methods section. We have also modified the Results section to remove ‘strong’ words in the text. Wherever appropriate, we have pointed out possible confounders and limitations of the current study in the Discussions section.

6) The authors need to revise the main text to be more careful about the language used to avoid conflating correlation with causation. Words like "impact", "reinforced by", "influence", etc. imply that there is causal information where none exists.

As mentioned in the previous response, we have made our results narrative much more pragmatic in the current version of the manuscript, for example in the Introduction.

7) The full dataset analyzed here needs to be made publicly available prior to publication. It's not sufficient to ask readers to re-assemble the full meta-analysis on their own.

The key in-house source codes used in this meta-analysis have been provided as Supplementary file 4. The shotgun data of the ELDERMET is available for download from the ELDERMET website at http://eldermet.ucc.ie/temp1/eldermet_shotgun_data_filtered_all_sample.tar. We have now included this database information in the data availability statement of the manuscript.

8) Add analyses wherein each individual study is analyzed separately, as opposed to the current analysis which involves merging data across all the studies. The current approach has the downside of being influenced by the larger studies, whereas the former would allow for statements about the degree to which associations with age are reproducible across studies. That said, I'm not sure this is possible given the nature of the data. At a minimum, it would help to have some supplemental figures that depict the distribution of ages within and across studies.

We appreciate the reviewers’ suggestion to perform the analyses within individual study cohorts to show which results are reproduced within cohorts and those that are cohort specific. However, as pointed by the reviewers, and shown in Figure 1C and 1E, each of the individual cohorts has biases for samples belonging to certain age-groups. Four of the eight datasets analysed in the study have a significant variation in the age of diseased and control individuals. However, for CRC, we had additional validation cohorts and we have tested these aspects extensively in the validation cohorts (Figure 3; subsection “Age-specific changes in the directionality of taxon abundance alterations for specific diseases, and the microbiome response shared by multiple diseases”).

9) The Discussion section really nails a critical confounder, drug use, which could explain a lot of what is described here. Diet and lifestyle are also likely contributors to these associations. While I don't think these data can be used to de-couple these factors, it would be good to be more explicit about this issue in the abstract and introduction. As written, the reader might take away the possibly false conclusion that aging itself (independent of diet, drugs, etc) is associated with the gut microbiome.

We appreciate the reviewers’ suggestion and have added appropriate statements in the Introduction that describe these aspects. Specifically, we have mentioned that these metadata that can have an influence on gut microbiome but not available in curatedMetagenomicData (Introduction).

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Essential revisions:

Two remaining concerns about the statistic used need to be addressed prior to publication.

1) We appreciate seeing the distributions of AUCs that the authors generated, and this does help to make their point more convincing. However, now that we understand better what they did, we're not sure that the test they used is appropriate. It seems that the authors are partitioning studies into age groups, training on a randomly-sampled subset of one age group, then testing on the remaining held-out samples from the 1-2 other age groups. This is all good practice so far. The issue is that the authors seem to be doing this repeatedly to get a distribution of AUCs per cohort, then testing for significant differences between the AUC distributions per cohort, using a Mann-Whitney U test. The problem with this is that AUCs within a cohort are not actually independent of each other (which the M-W test requires), because some classifiers will have been trained on the same subsamples. This could make the distribution of AUCs look narrower than it really is. Further, the same classifier is being used on each age group, which means there are also correlated errors across cohorts. Testing for a significant difference between AUCs is actually a pretty subtle and difficult problem, which is another reason we prefer making statements about significance using explicit statistical models like logistic regression, whose properties are better understood.

If the authors really want to test for significant differences between these models, we would encourage them instead to use a permutation test: i.e., shuffle the labels of the age cohorts repeatedly, then use the empirical distribution of differences in AUCs between cohorts to test for significance. This should at least ensure that the correlations introduced by sampling and by testing the same classifier on multiple cohorts will also be present in the null distributions.

While we appreciate the reviewer’s thoughtful response and we agree that the AUCs are not truly independent and so any statistical test will give a P-value that is lower than if we had many truly independent (non-overlapping) cohorts, this lack of true independence would only be problematic in a situation where the observed biological (size-) effect is small and replication is not seen across country and disease cohorts. However, we have replicated these findings across multiple datasets and have observed large effect sizes (0.04 – 0.38 difference in AUC). Even though errors can be correlated across age cohorts, it is unlikely that they are correlated across country and disease cohorts; we still observe the difference in same age-group classification versus different age-group classification. Furthermore, if the analysis was affected by a number of ‘difficult to classify’ samples, the difficult to classify samples would be difficult to classify independently of the prediction model used, and so we would not expect it to lead to the observed differences.

However, in line with the reviewers’ suggestions, we have modified the strategy used to test whether our RF-based models show significant age-group-specific variations in their prediction accuracy for the various diseases, using two different approaches.

In the first approach, we created two ‘re-sampled’ tests sets. While one contained Y diseased and Y control individuals from the same age-group (but not included in the training sub-set) (Same Age-group test set), the other contained Y diseased and Y control individuals from the other two age-groups (Different Age-group test set) (with Y defined as in Figure 2—source data 1). We tested the classifier on each test set and computed the AUCs. Testing the same classifier on both test sets ensured that the observed variations in disease prediction performance was not due to differences in the subject sub-samples used to create the classification models. Furthermore, to ensure that we don’t have biases introduced because of the selection of test sets (same age-group, different age-group), we repeated above steps 20 times (per classifier in each iteration) and computed the median AUCs for both the Same Age-group classification and the Different age-group classification. These median AUCs obtained for the Same Age-group and Different age-group classification for each of the 100 iterative sub-sampled classifiers were compared using Wilcoxon Signed Rank tests (to check if the performance of the classifiers significantly varied when tested on the same or different age-groups).

In the second approach, using Permutation test framework, we tested whether observed difference in classification AUCs (i.e. AUC for Same Age-group classification – AUC for Different Age-group classification) was significantly different than what would be expected by random chance. For this, we needed null distribution of empirical differences of AUC for the same sub-sampled classifier. For this purpose, for each of the 20 iterations corresponding to each of the 100 sub-sample based RF classifier models (as described in the previous paragraph), we first merged the two ‘re-sampled’ test sets (Same Age-group test set and Different Age-group test set), then permuted the age-group labels of the subjects, creating two ‘Permuted’ test tests (Permuted test set 1 and 2), tested the classifier model for each of the Permuted test sets, and finally computed the AUC differences obtained for the same classifier between the two permuted test sets. For each of the RF classifiers (across the 100 iterations), we computed the medians of the differences of AUCs (for the two permuted test sets) across the 20 iterations. The median difference of the AUCs obtained for the actual test (for Same Age-group – Different Age-group) and the permuted tests (Permuted Test set 1 – Permuted test set 2) obtained for the 100 iterations were then compared with Wilcoxon signed rank-tests. The objective of these permutation tests was to reduce the effects of correlated errors associated with certain ‘aberrant’ samples in influencing the disease prediction performance of RF classifiers for specific age-groups. This permutation test is expected at least ensure that the correlations introduced by sampling and by testing the same classifier on multiple cohorts will also be present in the null distributions. For both the approaches, the p-values of comparison across age-groups were corrected using Holms’ correction.

These details have now been included in subsection “Disease classification using Random Forest (RF) Models”.

The Results section has also been modified accordingly and provided in Figure 2 (for the first approach) and Figure 2—figure supplement 2 (for the permutation test based approach).

The complete schematic summary of this approach has now been provided in Figure 2—figure supplement 1.

For uniformity, we have also used the same strategy for the validation of age-specific disease association trends in the CRC cohorts in Figure 4A (earlier Figure 3) and newly added Figure 4—figure supplement 1. These results have been described in subsection “Replicability of the age-centric microbiome-disease signatures across multiple cohorts in CRC2”.

We have also additional source codes for this analysis that can be tested on the cohorts from the curatedMetagenomicData as well as additional in-house cohorts.

2) We also have one more substantive concern with Figure 5. We appreciate the authors did not drop low nominal p-values, but it is also not appropriate to use a fold-change cutoff before applying p-value corrections. Any filtering that uses information about the difference between groups (which includes fold-change cutoffs) is statistical double-dipping and will tend to inflate significance. You can either apply the fold-change cutoff after p-value correction or apply a filter that doesn't depend on knowledge about which samples belong to which groups, like the overall variance or the number of non-zero observations.

We appreciate the reviewers’ concern and have now removed the fold-change cut-off. We have now modified our identification strategy to identify the frailty-associated metabolites. There are two specific properties of these metabolite profiles that were important:

These metabolite profiles should be enriched in their detection in the frailty associated taxonomic markers. But not all metabolite profiles associated with these markers may be associated with frailty.

These metabolite profiles should also independently show significant negative associations with Frailty scores (if we take the cumulative abundance of all species known in literature to have the given metabolite profile).

Thus, in the current manuscript version, we identified the frailty-linked metabolites associated with the eight taxonomic markers of frailty using a two-step strategy. First, we performed a correlation analysis of each metabolite profile (i.e. the cumulated abundance of taxa previously associated in literature with a given metabolic capability as obtained above) with FIM scores and identified metabolite profiles that showed significant association with FIM scores (Spearman Rho; FDR of less than 0.15). Next, we identified which of these identified metabolite profiles were detected in the taxonomic markers of frailty (based on previous literature) at a rate significantly higher than the background detection (using our Fishers’ exact test approach with FDR corrected P < 0.20.

This has been summarized in subsection “Creating metabolite species maps and obtaining the frailty-associated metabolic signature of a given group of species”.

In the first step, a total of 82 metabolite profiles were observed to have significant association with FIM scores (Spearman Rho; FDR of less than 0.15) (shown in newly added Figure 5—figure supplement 2). We observed that this association analysis of metabolite profiles reflects frailty-associated changes with respect to bioavailability of specific compounds, many of which have been previously shown to have corresponding associations with health, thereby corroborating our findings (Claesson et al., 2012). Specifically, the onset of frailty is observed to be associated with an increase of SCFA consumption by gut-bacteria (accompanied by a concomitant decrease of the production of the SCFA butyrate), increased consumption of the beneficial amino acid Tryptophan as well as increased production of the T2D-linked amino acid Threonine.

This has been summarized in subsection “Reproducible association of the G1 disease-positive markers with increased frailty in elderly individuals from the ELDERMET cohort”.

In the next step, we focussed on the group of frailty marker taxa and identified 13 metabolic profiles that were significantly associated with the eight frailty-marker taxa. This subset of 13 metabolite profiles alone could predict frailty with a R value of 0.60 (between the actual and predicted FIM values), which is significantly higher than that obtained after removing these features from the metabolite profile (subsection “Reproducible association of the G1 disease-positive markers with increased frailty in elderly individuals from the ELDERMET cohort”).

In summary, 12 of our earlier identified 16 metabolites are retained in this list. We don’t detect Acetate Production, Xylose Consumption, Dextrin Consumption and CO2 Production in this new list. However, the most important observation is the addition of Trimethylamine Production which agrees perfectly well with our detection of high Choline consumption, an increased abundance of CutC enzyme as well as further indicating the detrimental effects of these frailty-associated taxonomic markers. Figure 6 (earlier Figure 5) has now been modified to include these results.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

It is unclear in the main text why 2 different approaches are needed – especially since the last paragraph of subsection “Influence of age on the microbiome and microbiome-disease signatures” only refers to the results of the permutation based strategy. Seems like might be more straightforward to just report the latter, but at the least the results of both and not just one of the approaches should be reported if both are described.

We would like to note that we had already mentioned the results using both the approaches (Please refer to the last paragraph of subsection “Influence of age on the microbiome and microbiome-disease signatures”). This section reads as follows:

“The data revealed large differences across age groups for the various diseases. Specifically, in 10 of the 13 disease-age group scenarios (covering five diseases), classifiers trained and tested on the same age-group had significantly higher disease prediction AUC than when tested on different age-groups (Figure 2), with the improvement of classification performance significantly higher than would be expected by random chance (obtained using the permutation test based strategy) (Figure 2—figure supplement 2). This confirmed that microbiome-disease associations had age-centric trends.”

However, for the specific case of CRC and Polyps, we have now modified the latter half of subsection “Age-specific changes in the directionality of taxon abundance alterations for specific diseases, and the microbiome response shared by multiple diseases”. The section reads as below:

“CRC and Polyps were characterized by noticeably similar age-specific trends wherein the elderly age-groups had noticeable decrease in classification AUCs (that is lower AUCs for classifiers trained or tested on the elderly age-groups), indicating that the microbiome-disease signatures for these diseases in the elderly age-groups are weaker (Figure 2). For these two diseases, the AUC difference between the Same Age-group and Different Age-group disease classification was also reduced in the elderly (in the case of Polyps, this difference was not significantly greater that what would be expected by random chance) (Figure 2—figure supplement 2).”

In subsection “Influence of age on the microbiome and microbiome-disease signatures”, where authors say "In summary, in each disease-age-group scenario, we used two different approaches. " They should qualify for what – "e.g. we used two different approaches to assess whether disease classification performance was significantly different between same age group classification and different age group classification".

We have now clarified this.

In subsection “Influence of age on the microbiome and microbiome-disease signatures”: "is significantly different from what would be expected by random (null distribution) " should briefly quality "generated by permuting the age-group labels of the subjects".

We have now clarified.

There was also a minor concern with the permutation test. The authors seem to be getting one average true or permuted AUC for each of the 100 resampled classifiers, instead of averaging across classifiers and getting one AUC per permutation (comparing the "true" value to this null to get a p-value). Because of non-independence between the training and test samples the classifiers used, the author's procedure should yield narrower AUC distributions, which could inflate significance. A back of the envelope estimate suggests an average overlap between classifiers of around 10-20% of the training + test samples. Given the consistency of the story and the magnitude of the differences the authors observe, though, as well as the computational time cost of doing enough permutations to get an accurate p-value, we leave it up to the authors whether they want to 'inoculate themselves against' this particular criticism.

We have now explicitly mentioned and discussed this concern (as mentioned by the reviewer in the last paragraph) of the Discussions section as:

“The last concern is methodological and pertains to the non-independence between the training samples in the iterative RF classifiers generated for each disease-age-group scenario. Classifiers generated in each iteration are likely to share overlap of training (and/or testing) samples, thereby potentially resulting in narrower AUC distributions which could inflate significance values. However, the magnitude of the differences as well as consistency of the results (using multiple methods and study cohorts) clearly indicate the reliability of the results obtained in this study.”

We hope the clarification provided in the above paragraph addresses the reviewers’ concern regarding the over-interpretation of the results obtained using the methodology adopted in the current study.

Several different FDR cutoffs are used (0.15, 0.20, 0.25) with no explanation. Figure 6D seems particularly arbitrary in its use of two different thresholds, neither of which is that common (0.15 and 0.20). For that figure, in the absence of a really good reason to pick those specific cutoffs, it would probably be more transparent to just pick one standard threshold (e.g. 0.25) and to just make it really clear that Figure 6D represents a selection of the most significant of the resulting hits. The authors would of course also need to report how many total hits were significant at this threshold, and to make the full list available.

We have now adopted a single universal standard threshold of FDR < 0.25 for both stages of the identification of frailty-marker associated detrimental metabolites (Spearman Correlation associating the Metabolite Profiles with FIM values, followed by Fishers’ exact test analysis associating the FIM-associated Metabolite profiles identified in the Spearman Correlation analysis with the Frailty-associated taxonomic markers). Given this modification of the approach, the full list of metabolite profiles showing an association with FIM scores shows a slight change. Accordingly, Figure 6—figure supplement 2 that shows the full list of these metabolite profiles has been modified. However, the final list of 13 metabolite profiles shown in Figure 6D does not change even after increasing the threshold to FDR < 0.25.

The authors should pick either one of Spearman or Kendall distances; using both in different places is confusing. (Bray-Curtis is another option for calculating divergence between microbiome abundance profiles.)

We have now replaced Kendall distances, and used Spearman Distance measures instead, throughout the manuscript. Accordingly, boxplot of Figure 4C has been modified to show the Spearman distances of the feature rank profiles for different age-groups across the training cohorts.

https://doi.org/10.7554/eLife.50240.sa2

Article and author information

Author details

  1. Tarini S Ghosh

    APC Microbiome Ireland, University College Cork, Cork, Ireland
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Visualization, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9570-0365
  2. Mrinmoy Das

    1. APC Microbiome Ireland, University College Cork, Cork, Ireland
    2. School of Microbiology, University College Cork, Cork, Ireland
    Contribution
    Resources, Data curation
    Competing interests
    No competing interests declared
  3. Ian B Jeffery

    APC Microbiome Ireland, University College Cork, Cork, Ireland
    Contribution
    Conceptualization, Formal analysis, Supervision, Validation, Visualization
    Competing interests
    No competing interests declared
  4. Paul W O'Toole

    1. APC Microbiome Ireland, University College Cork, Cork, Ireland
    2. School of Microbiology, University College Cork, Cork, Ireland
    Contribution
    Conceptualization, Supervision, Visualization, Project administration
    For correspondence
    pwotoole@ucc.ie
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5377-0824

Funding

Science Foundation Ireland (APC/SFI/12/RC/2273)

  • Tarini S Ghosh
  • Paul O'Toole

Science Foundation Ireland (13/SIRG/2128)

  • Ian B Jeffery

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors are funded in part by Science Foundation Ireland (APC/SFI/12/RC/2273) in the form of a research centre, APC Microbiome Ireland. IBJ was supported by a Science Foundation Ireland grant (13/SIRG/2128).

Senior Editor

  1. Wendy S Garrett, Harvard T.H. Chan School of Public Health, United States

Reviewing Editor

  1. Peter Turnbaugh, University of California, San Francisco, United States

Reviewers

  1. Catherine A Lozupone, University of Colorado Denver - Anschutz Medical Campus, United States
  2. Peter Turnbaugh, University of California, San Francisco, United States

Publication history

  1. Received: July 16, 2019
  2. Accepted: February 10, 2020
  3. Version of Record published: March 11, 2020 (version 1)

Copyright

© 2020, Ghosh et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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