Gut microbial diversity changes throughout the human life span and is known to be associated with host sex. We investigated the association of age, sex, and gut bacterial alpha diversity in three large cohorts of adults from four geographical regions: subjects from the United States and United Kingdom in the American Gut Project (AGP) citizen-science initiative and two independent cohorts of Colombians and Chinese. In three of the four cohorts, we observed a strong positive association between age and alpha diversity in young adults that plateaued after age 40 years. We also found sex-dependent differences that were more pronounced in younger adults than in middle-aged adults, with women having higher alpha diversity than men. In contrast to the other three cohorts, no association of alpha diversity with age or sex was observed in the Chinese cohort. The association of alpha diversity with age and sex remained after adjusting for cardiometabolic parameters in the Colombian cohort and antibiotic usage in the AGP cohort. We further attempted to predict the microbiota age in individuals using a machine-learning approach for the men and women in each cohort. Consistent with our alpha-diversity-based findings, U.S. and U.K. women had a significantly higher predicted microbiota age than men, with a reduced difference being seen above age 40 years. This difference was not observed in the Colombian cohort and was observed only in middle-aged Chinese adults. Together, our results provide new insights into the influence of age and sex on the biodiversity of the human gut microbiota during adulthood while highlighting similarities and differences across diverse cohorts.
IMPORTANCE Microorganisms in the human gut play a role in health and disease, and in adults higher gut biodiversity has been linked to better health. Since gut microorganisms may be pivotal in the development of microbial therapies, understanding the factors that shape gut biodiversity is of utmost interest. We performed large-scale analyses of the relationship of age and sex to gut bacterial diversity in adult cohorts from four geographic regions: the United States, the United Kingdom, Colombia, and China. In the U.S., U.K., and Colombian cohorts, bacterial biodiversity correlated positively with age in young adults but plateaued at about age 40 years, with no positive association being found in middle-aged adults. Young, but not middle-aged, adult women had higher gut bacterial diversity than men, a pattern confirmed via supervised machine learning. Interestingly, in the Chinese cohort, minimal associations were observed between gut biodiversity and age or sex. Our results highlight the patterns of adult gut biodiversity and provide a framework for future research.


The human gut microbiota is a highly diverse ecosystem that is extremely variable among individuals (1). This microbial community may play a key role in human health and disease (2). Since the gut microbiota may be pivotal to the development of microbial therapies, understanding the factors that shape overall gut microbiota biodiversity over the different human life stages is of utmost interest.
There is increasing evidence suggesting that host genes, gene expression patterns, environmental exposures (including medication and diet), and lifestyle factors play an important role in delimiting the boundaries of microbial diversity in the gut (3, 4). While a detailed longitudinal study of the interplay of each of these factors would be scientifically, logistically, and financially challenging, the chronological age of the host may be conceived of as a proxy variable that represents the accumulation of these effects for a given individual. Several studies have reported a positive correlation between age and gut microbiota alpha diversity from birth to adulthood (58). Likewise, it has been shown that alpha diversity is maintained in old age, until comorbidities contribute to its decline (9). Another intriguing host-associated pattern identified in humans and rodents is the link between the gut microbiota and biological sex. Several studies have reported that women have higher microbial diversity than men and that sex differences in microbial composition emerge after puberty (8, 1013). These differences may contribute to the sexual dimorphism of autoimmune (12, 14, 15) and neuroimmune (16, 17) diseases. Therefore, it is key to consider the impact of age and sex differences in different human populations to adequately discriminate changes and variations in the microbiome of individuals.
To better understand how the age and sex of the host relate to the diversity of the gut microbiota during adulthood, we explored the association of these factors using data from individuals in three cross-sectional studies from four geographical origins, including the citizen-science American Gut Project (AGP), comprised of individuals from the United States and the United Kingdom (4); a cohort of individuals from China (18); and a study of community-dwelling adults from Colombia (19).


The basic characteristics of the individuals from the four cohorts, stratified by sex and age group, are summarized in Table 1. We defined adults as individuals between 20 and 69 years of age and divided the age groups by the middle point of this range (i.e., 45 years); subjects above 70 years of age were excluded from the analysis.
TABLE 1 General characteristics of the participants of the included cohortsa
Cohort and characteristicYoung adults (ages 20–45 yr)Middle-aged adults (ages 46–69 yr)b
AGP, U.S.    
    No. of subjects627644734583
    Age (yr)34.60 (6.79)33.76 (6.61)56.13 (6.37)57.13 (6.41)
    SV richness113.80 (33.04)110.95 (31.51)120.40 (0.89)119.0 (0.78)
    Shannon index4.87 (0.83)4.83 (0.80)4.98 (0.89)5.01 (0.78)
AGP, U.S. antibiotic consumers    
    No. of subjects1368314791
    Age (yr)33.0 (7.75)58.07 (6.56)34.71 (6.87)58.35 (6.52)
    SV richness100.38 (27.61)97.80 (31.85)107.07 (30.06)108.71 (30.11)
    Shannon index4.64 (0.74)4.70 (0.83)4.72 (0.80)4.83 (0.85)
AGP, U.K.    
    No. of subjects195173344224
    Age (yr)35.90 (6.02)36.40 (6.32)56.45 (6.68)57.75 (6.85)
    SV richness132.0 (31.69)122.60 (32.38)142.30 (36.27)139.10 (36.23)
    Shannon index5.27 (0.69)5.05 (0.92)5.36 (0.83)5.29 (0.80)
    No. of subjects9466701,8261,521
    Age (yr)35.16 (6.73)34.88 (7.07)56.6 (6.59)57.36 (6.75)
    SV richness101.80 (27.90)99.66 (26.48)99.41 (28.67)101.4 (28.50)
    Shannon index4.47 (0.85)4.40 (0.84)4.36 (0.93)4.35 (0.95)
    No. of subjects1431338378
    Age (yr)33.83 (7.21)34.21 (6.98)52.48 (4.14)52.90 (4.42)
    SV richness120.41 (30.21)110.71 (31.06)123.33 (32.75)116.13 (33.95)
    Shannon index4.60 (1.05)4.48 (1.12)4.73 (0.99)4.45 (1.13)
Cardiometabolic risk scale−1.14 (3.07)0.64 (3.67)−0.36 (3.06)1.39 (2.71)
Values are given as the mean (SD).
The ages of the Colombian individuals ranged from 20 to 62 years.
To assess changes in alpha diversity with age during adulthood, we fit a simple linear regression model and a regression model with linear splines, in which the model is fit as two consecutive segments (20 to 45 years and 46 to 69 years; see Fig. S1 in the supplemental material). We then evaluated the goodness of fit of each model using the Akaike information criterion (AIC), which indicated that changes in alpha diversity are better explained by distinguishing between young adults (20 to 45 years of age) and middle-aged adults (46 to 69 years of age). In the U.S., U.K., and Colombian cohorts, we observed a positive but nonlinear association between alpha-diversity measures and age in both women and men. Loess curves fit independently by sex showed an inflection point after 40 years of age in each of these cohorts (Fig. 1A to C). In contrast, we did not observe such a pattern in the Chinese cohort, in which alpha diversity displayed a slight decrease with age (Fig. 1D).
FIG 1 Gut microbiota richness is nonlinearly associated with age and differs between women and men in multiple populations: United States (n = 2,588) (A), United Kingdom (n = 936) (B), Colombia (n = 437) (C), and China (n = 4,963) (D). (Left) Sequence variant (SV) richness in adults ages 20 to 69 years (the age of the Colombians ranged from 20 to 62 years); lines indicate the relationship of richness with age after Loess smoothing for women and men separately. (Right) SV richness in young (age, 20 to 45 years) and middle-aged (age, 46 to 69 years) adults; lines indicate the linear regression fit for women and men separately.
Next, for each population, we fit linear regression models to examine associations of microbial diversity, age, and sex in each age group separately. In both the U.S. and U.K. cohorts, we observed a positive relationship between microbial richness and age for both sexes in young adults (adjusted P value [P-adjust], <0.001 for the U.S. cohort and <0.001 for the U.K. cohort), but not in middle-aged adults (P-adjust, 0.474 for the U.S. cohort and 0.216 for the U.K. cohort) (Fig. 1A and B). In addition, after accounting for age, differences in sequence variant (SV) richness tended to be higher in young adults (for the U.S. cohort, difference between men and women [Δmen − women] = −3.3 and P-adjust = 0.134; for the U.K. cohort, Δmen − women = −9.84 and P-adjust = 0.024) than in middle-aged adults (for the U.S. cohort, Δmen − women = −1.3 and P-adjust = 0.484; for the U.K. cohort, Δmen − women = −3.7 and P-adjust = 0.270). Similar results were observed when we assessed taxon evenness using the Shannon index (Fig. S2). Similar to the U.S. and U.K. cohorts from the AGP, we identified a positive relationship between richness and age in the Colombian cohort in young adults of both sexes (P-adjust = 0.008) but not in middle-aged adults (P-adjust = 0.722) (Fig. 1C). Likewise, there was a difference in overall SV richness between the sexes in young adults (Δmen − women = −10.0; P-adjust = 0.024) but not in middle-aged adults (Δmen − women = −7.3; P-adjust = 0.225). In contrast to the U.S., U.K., and Colombian cohorts, we observed no association between microbiota alpha diversity and age in young-adult or middle-aged-adult Chinese (P-adjust > 0.1 for both comparisons) (Fig. 1D). Men in the Chinese cohort tended to have lower SV richness than women as young adults, yet the difference was not significant (for young adults, Δmen − women = −2.14 and P-adjust = 0.194; for middle-aged adults, Δmen − women = 2.04 and P-adjust = 0.107). We did not find evidence of an interaction between age and sex on microbial diversity in the studied cohorts for young or middle-aged adults with either of the diversity measures, after correcting for multiple comparisons (P-adjust > 0.15 in all cases). In all cohorts apart from the Chinese, the proportion of variance in alpha diversity explained by age and sex was moderate, yet it was consistently higher in younger adults than in middle-aged adults (Table S1).
Given that gut microbial diversity may be affected by factors such as antibiotic use or the cardiometabolic health of the host, we replicated the above-described analyses in cohorts in which we observed the patterns, making use of publicly available metadata. To test whether the consumption of antibiotics modified the observed pattern, we performed the above-described analyses on a set of 457 individuals (283 women and 174 men) from the U.S. cohort of the AGP that reported having consumed antibiotics in the 6 months prior to enrollment. We observed a lower SV richness in these individuals than in those that did not consume antibiotics (Table 1). Among the participants that consumed antibiotics in the past 6 months, we observed a similar tendency for alpha diversity to increase in the younger group and plateau in middle-aged individuals, with women having higher diversity than men, although there was a lack of statistical significance (Fig. 2). Likewise, we replicated the analyses in the Colombian cohort after introducing a composite measure of the cardiometabolic health of the subjects as a covariate into the linear models; after we adjusted the analyses for the cardiometabolic health score, the observed patterns were similar (Fig. 3).
FIG 2 Antibiotic consumption has a limited association with the patterns of alpha diversity in U.S. adults that had consumed antibiotics 6 months prior to enrollment (n = 457). (A) SV richness; (B) Shannon index. (Left) Alpha-diversity metrics in women and men ages 20 to 62 years; lines indicate the relationships of richness with age after Loess smoothing. (Right) Alpha-diversity metrics in young (age, 20 to 45 years) and middle-aged (age, 46 to 69 years) adults; lines indicate the linear regression fit for women and men separately.
FIG 3 Adjusting alpha diversity by cardiometabolic health does not affect the observed patterns in Colombian adults (n = 437). (A) Residuals of SV richness; (B) residuals of the Shannon index. (Left) Adjusted alpha-diversity metrics in women and men ages 20 to 62 years; lines indicate the relationships of richness with age after Loess smoothing. (Right) Adjusted alpha-diversity metrics in young (age, 20 to 45 years) and middle-aged (age, 46 to 62 years) adults; lines indicate the linear regression fit for women and men separately.
To examine whether similar age- and sex-associated patterns would be observed when analyzing the relative taxon abundance in the gut microbiota, rather than using only alpha-diversity measures, we used a supervised machine-learning approach to compare the composition of the gut microbiota of the subjects of the different populations. We subdivided each cohort by sex, determined the SVs shared by both groups, and used their relative abundances and the chronological age at the time of sample collection of the host to fit a random forest (RF) regression model. Two models were built for women and men aged 20 to 69 years; each was trained using the data for one sex and tested on the other. For each subject, we calculated the relative microbiota age as the difference between its microbiota age and the microbiota age of the interpolated spline fit of an individual of the opposite sex at the same chronological age. Our results from random forest regressions indicated that the composition of the gut microbiota explained a low to moderate proportion of variance in chronological age, which varied by population and sex (Table S2).
We used 1,494 shared SVs between women and men to build the RF model of the U.S. cohort (Fig. 4A). We found that men exhibited a lower relative microbiota age than women (in the women-to-men model, the difference between women and men [Δwomen − men] = 0.81 years; P-adjust < 0.001, Wilcoxon rank-sum test; Fig. 4B, top), suggesting that sex is associated with the adult gut microbial aging process. To validate this finding, we also trained an RF model in the men and then applied it to the women (Fig. 4A, bottom); we found that women had a higher microbiota age (in the men-to-women model, Δwomen − men = 1.0 years and P-adjust < 0.001; Fig. 4B, bottom). To establish whether these trends were present in different age groups, we then examined the sex-dependent association of microbiota age in young and middle-aged adults separately. In the young-adult group, we selected the 1,311 shared SVs between both sexes to build the RF model for women and then applied it to predict the microbiota age of men. We found that young women exhibited a slightly higher relative microbiota age than men (Δwomen − men = 0.32 years, P-adjust < 0.001; Fig. 4C and D, top). Similar results were observed when we assessed the microbiota age in the middle-aged-adult group, in which we used the 1,601 shared SVs between sexes to build the RF model as described above. The microbiota age was higher in women than in men (Δwomen − men = 0.48 years, P-adjust < 0.001; Fig. 4C and D, top). Furthermore, such sex differences in microbiota age were not affected when we applied the men’s model to women (Fig. 4C and D, bottom). Likewise, in the U.K. cohort, we found that the microbiota age was higher in women than in men (women-to-men model, Δwomen − men = 0.51 years and P-adjust = 0.002; men-to-women model, Δwomen − men = 0.96 years and P-adjust < 0.001; Fig. 4E and F), using 1,613 SVs found in either the women’s or men’s microbiota for building and applying RF models. In addition, we observed significant or borderline significant differences in relative microbiota age between sexes in young adults (women-to-men model, Δwomen − men = 0.15 years and P-adjust = 0.186; men-to-women model, Δwomen − men = 0.36 years and P-adjust = 0.028; Fig. 4G and H) and middle-aged adults (women-to-men model, Δwomen − men = 0.41 years and P-adjust < 0.001; men-to-women model, Δwomen − men = 0.25 years and P-adjust < 0.091; Fig. 4G and H). In the Colombian cohort, we used 1,074 SVs shared between sexes to build the RF model; similar yet nonsignificant trends were observed between microbiota age and sex in the nonstratified analyses (women-to-men model, Δwomen − men = 0.38 years and P-adjust = 0.173; men-to-women model, Δwomen − men = 3.0e−05 years and P-adjust > 0.9; Fig. 4I and J) and in the young-adult group (women-to-men model, Δwomen − men = 0.18 years and P-adjust = 0.189; men-to-women model, Δwomen − men = 0.02 years and P-adjust = 0.292; Fig. 4K and L) and the middle-aged-adult group (women-to-men model, Δwomen − men = 0.27 years and P-adjust = 0.186; men-to-women model, Δwomen − men = 0.27 years and P-adjust = 0.873; Fig. 4K and L). We used 1,279 SVs shared between sexes to build the RF models in the Chinese cohort. The association between microbiota age and sex was not consistent when we cross-tested the models (women-to-men model, Δwomen − men = −0.07 years and P-adjust = 0.468; men-to-women model, Δwomen − men = 0.45 years and P-adjust < 0.001; Fig. 4M and N). We did not observe significant associations in the young-adult group (women-to-men model, Δwomen − men = 0.09 years and P-adjust = 0.183; men-to-women model, Δwomen − men = −0.17 years and P-adjust = 0.028; Fig. 4O and P), whereas in the middle-aged-adult group, we observed sex-dependent differences in microbiota age, and such differences tended to be consistent in the cross-application of the models (women-to-men model, Δwomen − men = 0.08 years and P-adjust = 0.059; men-to-women model, Δwomen − men = 0.31 years and P-adjust < 0.01; Fig. 4O and P).
FIG 4 Gut microbiota age differs between women and men in multiple populations: United States (n = 2,588) (A to D), United Kingdom (n = 936) (E to H), Colombia (n = 437) (I to L), and China (n = 4,963) (M to P). For each population, the first set of panels (A, E, I, M) shows the microbiota age of women (orange) or men (blue), as calculated by a random forest (RF) model trained on the female (top scatter plots) or male (bottom scatter plots) subsets; lines indicate the spline fit. The second set of panels (B, F, J, N) shows the relative microbiota age (the difference of microbiota age of the interpolated spline fit based on the training data and microbiota age predicted in either training or test data) in women and men, which was derived from either an RF model trained on women and tested on men (top box plot) or an RF model trained on men and tested on women (bottom box plot). The third (C, G, K, O) and fourth (D, H, L, P) sets show results of analyses similar to those for the first two but are stratified by age group.
Next, from the RF model trained on each sex to predict age from gut microbial composition, we determined the number of SVs that minimized the 10-fold cross-validation error of the models. We found that the error of the simplified models increased sharply when less than 500 SVs were used (Fig. S3). Finally, we obtained the taxonomic classification of the 500 SVs with the highest RF importance score in at least one of the models (Table S3). Overall, we found that SVs belonging to the families Ruminococcaceae, Bifidobacteriaceae, Lachnospiraceae, Clostridiaceae, and Christensenellaceae consistently had high RF importance scores, although the values differed between populations and within populations between men and women.


In this study, we analyzed the association of gut microbial alpha diversity with age and sex in three large cross-sectional cohorts encompassing four geographically distinct community-dwelling adult populations. Our analyses indicate that age is positively associated with gut bacterial diversity in men and women, with greater diversity being seen in women than in men. Notably, this association occurs in young but not middle-aged adults. Consistent with these findings, the predicted microbiota age varied based on sex, with stronger associations being seen in young adults. It is worth underscoring that while we did not observe these patterns in all studied cohorts, it was widespread and robust to technical differences, and the alpha-diversity shifts were not modified by the cardiometabolic health of the host or the antibiotic consumption in the cohorts for which this information was available. These findings provide new insights into the development of the human gut microbiome in adulthood according to both age and sex and emphasize the importance of including chronological age and sex as covariates in analyses of the human gut microbiota.
While the most dramatic change in gut microbiota diversity occurs in early childhood (7, 8), its increase in adulthood has also been reported (20, 21). In the cohorts in which the pattern was present, we observed an increase in alpha-diversity measures in young adults; however, this trend halted at about age 40 years (Fig. 1 and Fig. S1 in the supplemental material). This finding agrees with a previous report, in which no significant differences in alpha diversity were found between middle-aged adults and septuagenarians (21). The diversity of the gut microbiota continues changing after the seventh decade of life; it has been shown that centenarians have a higher alpha diversity than middle-aged adults, though it remains unknown whether this is the cause or the effect of healthy aging (2022). However, gut microbiota diversity in the elderly can differ according to their community residence setting, as community dwellers have been shown to have a higher diversity than individuals in long-term residential care (23, 24).
Interestingly, the relationship between age and diversity was also linked with sex. Multiple studies have reported differences in the diversity and composition of the gut microbiota between female and male mice, which appear to be associated with a sex bias in the incidence of specific diseases, such as type 1 diabetes (12, 15), rheumatoid arthritis (14), and anxiety (25); sex-by-diet interactions have also been reported (26). While differences in alpha diversity between males and females were reported in humans and mice, we showed that the association between sex and alpha diversity was stronger in young adults than in middle-aged adults. In agreement with our results, no differences in alpha diversity were observed between women and men in a recent study in which the mean age of the participants was 60 years (27).
One of the most intriguing findings was the difference in gut microbiota richness between the sexes in young adults. This sex-dependent discrepancy suggests that women may enter adulthood with a more diverse gut microbiota, which plateaus at the same levels in both sexes by approximately age 40 years. The microbiota age models of young adults (ages 20 to 45 years) can explain about 2.5% more of the variance of chronologic age than those of middle-age adults. The establishment of different microbial communities in males and females may be mediated by sex hormones: female mice show a significant increase in alpha diversity during puberty (28), and differences in the composition of the microbiota increase with age but are eliminated by male castration (15). While little is known about the maturation of the human gut microbiota during puberty, we speculate that the differential hormonal milieu between women and men and the earlier timing of puberty in women may result in a more rapid diversification of the gut microbiota in women and that men only achieve the same level of diversification by middle age. Since our findings are based on cross-sectional data, future longitudinal studies are needed to disentangle age and birth cohort effects and the impact of factors such as steroid hormonal levels, pubertal transition, contraceptives, and lifestyle that may vary throughout life. Future research should also investigate specific microbial changes that may influence time-dependent sex differences on the biodiversity of the human gut microbiome.
While 3 of the 4 cohorts had an association between age, sex, and microbial alpha diversity, the Chinese cohort did not (Fig. 1 and Fig. S1), indicating that these associations are a widespread feature of the human gut microbiota whose universality remains an open question. The overall alpha diversity of this cohort, as measured by SV richness and the Shannon index, was lower than that of the other three cohorts. We also note that the exclusion criteria for this population were not the same as those for the populations in the other studies, with only a 1-month antibiotic exclusion and no stated exclusion of participants with diabetes or inflammatory bowel disease (18).
The striking similarity among the U.S., U.K., and Colombian cohorts with regard to age- and sex-dependent associations with microbial biodiversity arose despite the different geographical origins, sample sizes, and collection protocols of the studies. Moreover, we also found no apparent association of antibiotic use (U.S. or U.K. cohort; Fig. 2) or cardiometabolic health (Colombian cohort; Fig. 3) on the patterns observed in these cohorts, suggesting that the influence of age and sex on the microbiota may be similar in other ethnic and cultural groups beyond the influence of cardiometabolic disease and antibiotic consumption. Nevertheless, similar large-scale population studies should be performed or reanalyzed to determine the extent to which our results are generalizable to other populations, particularly in light of the findings for the Chinese cohort. Indeed, the contrast between the U.K., U.S., and Colombian cohorts and the Chinese cohort highlights the power of using large data sets and comparative analyses across cohorts to uncover subtle patterns and reveal novel insights not discernible in smaller studies. This is of critical importance, given the plausibility of population-specific disease signatures of the microbiome (18).


Cohort description.

Fecal samples were obtained from individuals in three independent cohorts from four geographical locations. (i) The AGP data set is composed of two cohorts with individuals from the United Kingdom (539 women and 397 men) and the United States (1,361 women and 1,227 men) (Table 1) consisting of healthy participants with a self-reported age of between 20 and 69 years, a body mass index (BMI) of between 18.5 and 30 kg/m2, and no history of inflammatory bowel disease, diabetes, or antibiotic use in the past year. (ii) A cohort of Chinese individuals comprised 2,772 women and 2,191 men aged 20 to 69 years with a BMI ranging from 18.5 to 30 kg/m2 and no antibiotic consumption reported 1 month prior to fecal sample collection; pregnant women and hospitalized, disabled, or critically ill individuals were not included in the study. (iii) A cohort of community-dwelling Colombians (226 women and 211 men) consisted of individuals 20 to 62 years of age enrolled in similar proportions according to BMI, city of residence, and age range (20 to 40 and 41 to 62 years); underweight participants, pregnant women, individuals who had consumed antibiotics or antiparasitics in the 3 months prior to enrollment, and individuals diagnosed with neurodegenerative diseases, current or recent cancer (<1 year), and gastrointestinal diseases were excluded. Details on the data acquisition, quality assessment, and processing of fecal samples from these three cohorts were previously described (4, 18, 19).

16S rRNA gene sequence processing.

The amplicon sequences of all three cohorts were uniformly processed following the same procedures previously described (4). Briefly, the V4 hypervariable region of the 16S rRNA gene was sequenced with the Illumina MiSeq platform. Raw sequences were clustered into sequence variants (SV) with deblur denoising (29) using the QIIME 2 package (30). Sequence counts were rarefied to 1,250 reads per sample across all samples to mitigate uneven sequencing depth. Downstream analyses in the Chinese cohort were replicated using a rarefaction depth of 5,000 reads per sample, and 3,600 reads per sample were used in the Colombian cohort, to exclude the effect of rarefaction depth on alpha-diversity estimation (data not shown). Note, however, that the sample collection and DNA extraction methods differed between the studies.

Statistical analyses.

SV richness and the Shannon index were calculated using QIIME 2, and statistical analyses were performed using R (v.3.4.3) software. The association of age and alpha diversity was measured with and without separate age groups by fitting linear models with linear splines (lspline [v.1.0] package of R) with a knot at the midpoint of the age range (45 years of age) and simple linear models, respectively. We assessed the goodness of fit of these models by means of the Akaike information criterion (AIC). Next, scatter plots of each alpha-diversity metric according to age were constructed, and then separate Loess curves for women and men were fit using the ggplot2 (v.3.0) package of R. Given the nonlinear association observed between alpha diversity and age, we subdivided the data sets into two separate age groups, 20 to 45 years (young adult) and 46 to 69 years (middle-aged adult), which were then used to fit linear models to test the associations of age (as a continuous variable) and alpha-diversity measures, stratified by sex; P values were adjusted for multiple comparisons using the Benjamini-Hochberg method (31).
Additionally, to account for the possible influence of participant antibiotic usage or cardiometabolic health on the observed associations, we conducted the following sensitivity analyses. For the former, we carried out the analyses using a separate group of individuals of the AGP cohort from the United States who had consumed antibiotics during the 6 months prior to their enrollment (283 women and 174 men). For the latter, we performed the analyses by adjusting the linear models for cardiometabolic risk in the Colombian cohort using a risk measure, which we termed the cardiometabolic risk scale (32). This was calculated using the sum of the z-scores of log-transformed waist circumference, triglyceride levels, insulin levels, diastolic blood pressure, and high-sensitivity C reactive protein levels; positive values of the score are associated with increased cardiometabolic health risk.
Random forest (RF) regression was used to regress the relative abundances of SVs in the gut microbiota of healthy women and men against their chronological age in each data set (randomForest R package of R) using the following parameters: ntree = 18,000 and mtry = p/3, where p is the number of input features (SVs). The microbiota age model was first trained on the training data set of female adults and was then applied to test the set of male adults, and vice versa. A smoothing spline function was fit between the microbiota age and the chronological age of the hosts for calculation of the relative microbiota age of the adults in the test sets to which the sparse model was applied. For a particular sample, the relative microbiota age was calculated as the difference between the microbiota age of a focal adult and the microbiota age of the interpolated spline fit of healthy female/male adults at the same chronological age. We further employed the Wilcoxon rank-sum test to compare the relative microbiota age between female and male groups in each data set. To determine the sex difference in microbiota age, we subdivided the data sets into the aforementioned age groups and repeated the analyses as described above in all age segments.

Data availability.

Processed SV tables are publicly available via the Qiita QIIME database (Colombian study, accession number 11993; AGP study, accession number 10317; China study, accession number 11757). The code and data required to reproduce the statistical analyses are available at https://github.com/jacodela/microbio_aDiv.


Data acquisition for the Colombian cohort was funded by the Grupo Empresarial Nutresa, Dinámica IPS, and EPS SURA. N.T.M. was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL141589 and by grants from the Mid-Atlantic Nutrition Obesity Research Center (P30DK072488) and the Foundation for Gender Specific Medicine. V.G.T. was supported by the National Institute of Child Health and Human Development through a cooperative agreement as part of the National Centers for Translational Research in Reproduction and Infertility (P50 HD012303). S.T.K. and V.G.T. received support from the Max Planck Institute for Developmental Biology in Tübingen, Germany. This work is supported by IBM Research AI through the AI Horizons Network and by the UC San Diego Center for Microbiome Innovation.
Some authors of this work collaborate through the Microbiome & Health Network.
While engaged in the research project, J.S.E. was employed by a food company. J.D.L.C.-Z., S.T.K., Y.C., N.T.M., R.E.L., S.H., A.D.S., R.K., D.M., and V.G.T. had no competing interests.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders of this work had no role in the study design, the collection, analysis, or interpretation of the data, the writing of the report, or the decision to submit the paper for publication.

Supplemental Material

File (msystems.00261-19-sf001.pdf)
File (msystems.00261-19-sf002.pdf)
File (msystems.00261-19-sf003.pdf)
File (msystems.00261-19-st001.docx)
File (msystems.00261-19-st002.docx)
File (msystems.00261-19-st003.csv)
ASM does not own the copyrights to Supplemental Material that may be linked to, or accessed through, an article. The authors have granted ASM a non-exclusive, world-wide license to publish the Supplemental Material files. Please contact the corresponding author directly for reuse.


Lloyd-Price J, Mahurkar A, Rahnavard G, Crabtree J, Orvis J, Hall AB, Brady A, Creasy HH, McCracken C, Giglio MG, McDonald D, Franzosa EA, Knight R, White O, Huttenhower C. 2017. Strains, functions, and dynamics in the expanded Human Microbiome Project. Nature 550:61–66.
Gilbert JA, Quinn RA, Debelius J, Xu ZZ, Morton J, Garg N, Jansson JK, Dorrestein PC, Knight R. 2016. Microbiome-wide association studies link dynamic microbial consortia to disease. Nature 535:94–103.
Foster KR, Schluter J, Coyte KZ, Rakoff-Nahoum S. 2017. The evolution of the host microbiome as an ecosystem on a leash. Nature 548:43–51.
McDonald D, Hyde E, Debelius JW, Morton JT, Gonzalez A, Ackermann G, Aksenov AA, Behsaz B, Brennan C, Chen Y, DeRight Goldasich L, Dorrestein PC, Dunn RR, Fahimipour AK, Gaffney J, Gilbert JA, Gogul G, Green JL, Hugenholtz P, Humphrey G, Huttenhower C, Jackson MA, Janssen S, Jeste DV, Jiang L, Kelley ST, Knights D, Kosciolek T, Ladau J, Leach J, Marotz C, Meleshko D, Melnik AV, Metcalf JL, Mohimani H, Montassier E, Navas-Molina J, Nguyen TT, Peddada S, Pevzner P, Pollard KS, Rahnavard G, Robbins-Pianka A, Sangwan N, Shorenstein J, Smarr L, Song SJ, Spector T, Swafford AD, Thackray VG, et al. 2018. American gut: an open platform for citizen science microbiome research. mSystems 3:e00031-18.
Hopkins MJ, Sharp R, Macfarlane GT. 2002. Variation in human intestinal microbiota with age. Dig Liver Dis 34(Suppl 2):S12–S18.
Mariat D, Firmesse O, Levenez F, Guimarăes V, Sokol H, Doré J, Corthier G, Furet J-P. 2009. The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age. BMC Microbiol 9:123.
Koenig JE, Spor A, Scalfone N, Fricker AD, Stombaugh J, Knight R, Angenent LT, Ley RE. 2011. Succession of microbial consortia in the developing infant gut microbiome. Proc Natl Acad Sci U S A 108(Suppl 1):4578–4585.
Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, Magris M, Hidalgo G, Baldassano RN, Anokhin AP, Heath AC, Warner B, Reeder J, Kuczynski J, Caporaso JG, Lozupone CA, Lauber C, Clemente JC, Knights D, Knight R, Gordon JI. 2012. Human gut microbiome viewed across age and geography. Nature 486:222–227.
Maffei VJ, Kim S, Blanchard E, IV, Luo M, Jazwinski SM, Taylor CM, Welsh DA. 2017. Biological aging and the human gut microbiota. J Gerontol A Biol Sci Med Sci 72:1474–1482.
Falony G, Joossens M, Vieira-Silva S, Wang J, Darzi Y, Faust K, Kurilshikov A, Bonder MJ, Valles-Colomer M, Vandeputte D, Tito RY, Chaffron S, Rymenans L, Verspecht C, De Sutter L, Lima-Mendez G, D’hoe K, Jonckheere K, Homola D, Garcia R, Tigchelaar EF, Eeckhaudt L, Fu J, Henckaerts L, Zhernakova A, Wijmenga C, Raes J. 2016. Population-level analysis of gut microbiome variation. Science 352:560–564.
Kozik AJ, Nakatsu CH, Chun H, Jones-Hall YL. 2017. Age, sex, and TNF associated differences in the gut microbiota of mice and their impact on acute TNBS colitis. Exp Mol Pathol 103:311–319.
Markle JGM, Frank DN, Mortin-Toth S, Robertson CE, Feazel LM, Rolle-Kampczyk U, von Bergen M, McCoy KD, Macpherson AJ, Danska JS. 2013. Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science 339:1084–1088.
Sinha T, Vich Vila A, Garmaeva S, Jankipersadsing SA, Imhann F, Collij V, Bonder MJ, Jiang X, Gurry T, Alm EJ, D’Amato M, Weersma RK, Scherjon S, Wijmenga C, Fu J, Kurilshikov A, Zhernakova A. 2018. Analysis of 1135 gut metagenomes identifies sex-specific resistome profiles. Gut Microbes 2018:1–9.
Gomez A, Luckey D, Yeoman CJ, Marietta EV, Berg Miller ME, Murray JA, White BA, Taneja V. 2012. Loss of sex and age driven differences in the gut microbiome characterize arthritis-susceptible 0401 mice but not arthritis-resistant 0402 mice. PLoS One 7:e36095.
Yurkovetskiy L, Burrows M, Khan AA, Graham L, Volchkov P, Becker L, Antonopoulos D, Umesaki Y, Chervonsky AV. 2013. Gender bias in autoimmunity is influenced by microbiota. Immunity 39:400–412.
Wallis A, Butt H, Ball M, Lewis DP, Bruck D. 2016. Support for the microgenderome: associations in a human clinical population. Sci Rep 6:19171.
Wallis A, Butt H, Ball M, Lewis DP, Bruck D. 2017. Support for the microgenderome invites enquiry into sex differences. Gut Microbes 8:46–52.
He Y, Wu W, Zheng H-M, Li P, McDonald D, Sheng H-F, Chen M-X, Chen Z-H, Ji G-Y, Zheng Z-D-X, Mujagond P, Chen X-J, Rong Z-H, Chen P, Lyu L-Y, Wang X, Wu C-B, Yu N, Xu Y-J, Yin J, Raes J, Knight R, Ma W-J, Zhou H-W. 2018. Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nat Med 24:1532–1535.
de la Cuesta-Zuluaga J, Corrales-Agudelo V, Velásquez-Mejía EP, Carmona JA, Abad JM, Escobar JS. 2018. Gut microbiota is associated with obesity and cardiometabolic disease in a population in the midst of Westernization. Sci Rep 8:11356.
Odamaki T, Kato K, Sugahara H, Hashikura N, Takahashi S, Xiao J-Z, Abe F, Osawa R. 2016. Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study. BMC Microbiol 16:90.
Biagi E, Nylund L, Candela M, Ostan R, Bucci L, Pini E, Nikkïla J, Monti D, Satokari R, Franceschi C, Brigidi P, De Vos W. 2010. Through ageing, and beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS One 5:e10667.
Kong F, Hua Y, Zeng B, Ning R, Li Y, Zhao J. 2016. Gut microbiota signatures of longevity. Curr Biol 26:R832–R833.
Claesson MJ, Jeffery IB, Conde S, Power SE, O’Connor EM, Cusack S, Harris HMB, Coakley M, Lakshminarayanan B, O’Sullivan O, Fitzgerald GF, Deane J, O’Connor M, Harnedy N, O’Connor K, O’Mahony D, van Sinderen D, Wallace M, Brennan L, Stanton C, Marchesi JR, Fitzgerald AP, Shanahan F, Hill C, Ross RP, O’Toole PW. 2012. Gut microbiota composition correlates with diet and health in the elderly. Nature 488:178–184.
Jeffery IB, Lynch DB, O'Toole PW. 2016. Composition and temporal stability of the gut microbiota in older persons. ISME J 10:170–182.
Bridgewater LC, Zhang C, Wu Y, Hu W, Zhang Q, Wang J, Li S, Zhao L. 2017. Gender-based differences in host behavior and gut microbiota composition in response to high fat diet and stress in a mouse model. Sci Rep 7:10776.
Org E, Mehrabian M, Parks BW, Shipkova P, Liu X, Drake TA, Lusis AJ. 2016. Sex differences and hormonal effects on gut microbiota composition in mice. Gut Microbes 7:313–322.
Haro C, Rangel-Zúñiga OA, Alcalá-Díaz JF, Gómez-Delgado F, Pérez-Martínez P, Delgado-Lista J, Quintana-Navarro GM, Landa BB, Navas-Cortés JA, Tena-Sempere M, Clemente JC, López-Miranda J, Pérez-Jiménez F, Camargo A. 2016. Intestinal microbiota is influenced by gender and body mass index. PLoS One 11:e0154090.
Kelley ST, Skarra DV, Rivera AJ, Thackray VG. 2016. The gut microbiome is altered in a letrozole-induced mouse model of polycystic ovary syndrome. PLoS One 11:e0146509.
Amir A, McDonald D, Navas-Molina JA, Kopylova E, Morton JT, Zech Xu Z, Kightley EP, Thompson LR, Hyde ER, Gonzalez A, Knight R. 2017. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems 2:e00191-16.
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet C, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Titus Brown C, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope E, Da Silva R, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley G, Janssen S, Jarmusch AK, Jiang L, Kaehler B, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, et al. 2018. QIIME 2: reproducible, interactive, scalable, and extensible microbiome data science. PeerJ PrePrints 6:e27295v1.
Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 57:289–300.
Guzman-Castaneda SJ, Ortega-Vega EL, de la Cuesta-Zuluaga J, Velasquez-Mejia EP, Rojas W, Bedoya G, Escobar JS. 2018. Gut microbiota composition explains more variance in the host cardiometabolic risk than genetic ancestry. bioRxiv.

Information & Contributors


Published In

cover image mSystems
Volume 4Number 427 August 2019
eLocator: 10.1128/msystems.00261-19
Editor: Sergio Baranzini, University of California, San Francisco


Received: 24 April 2019
Accepted: 30 April 2019
Published online: 14 May 2019


  1. 16S rRNA amplicon
  2. age
  3. diversity
  4. microbiome
  5. sex



Jacobo de la Cuesta-Zuluaga https://orcid.org/0000-0002-7369-992X
Department of Microbiome Science, Max Planck Institute for Developmental Biology, Tübingen, Germany
Department of Biology, San Diego State University, San Diego, California, USA
Yingfeng Chen
Department of Biology, San Diego State University, San Diego, California, USA
Vidarium—Nutrition, Health and Wellness Research Center, Grupo Empresarial Nutresa, Medellin, Colombia
Noel T. Mueller
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
Welch Center for Epidemiology, Prevention and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA
Ruth E. Ley
Department of Microbiome Science, Max Planck Institute for Developmental Biology, Tübingen, Germany
Department of Pediatrics, University of California, San Diego, La Jolla, California, USA
Shi Huang
Department of Pediatrics, University of California, San Diego, La Jolla, California, USA
Austin D. Swafford
Center for Microbiome Innovation, University of California, San Diego, La Jolla, California, USA
Rob Knight
Department of Pediatrics, University of California, San Diego, La Jolla, California, USA
Center for Microbiome Innovation, University of California, San Diego, La Jolla, California, USA
Department of Computer Science, University of California, San Diego, La Jolla, California, USA
Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
Varykina G. Thackray
Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Diego, La Jolla, California, USA


Sergio Baranzini
University of California, San Francisco


Address correspondence to Varykina G. Thackray, [email protected].
J.D.L.C.-Z. and S.T.K. contributed equally to this article.

Metrics & Citations



  • For recently published articles, the TOTAL download count will appear as zero until a new month starts.
  • There is a 3- to 4-day delay in article usage, so article usage will not appear immediately after publication.
  • Citation counts come from the Crossref Cited by service.


If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. For an editable text file, please select Medlars format which will download as a .txt file. Simply select your manager software from the list below and click Download.

View Options

Figures and Media






Share the article link

Share with email

Email a colleague

Share on social media

American Society for Microbiology ("ASM") is committed to maintaining your confidence and trust with respect to the information we collect from you on websites owned and operated by ASM ("ASM Web Sites") and other sources. This Privacy Policy sets forth the information we collect about you, how we use this information and the choices you have about how we use such information.
FIND OUT MORE about the privacy policy