Many US immigrant populations develop metabolic diseases post immigration, but the causes are not well understood. Although the microbiome plays a role in metabolic disease, there have been no studies measuring the effects of US immigration on the gut microbiome. We collected stool, dietary recalls, and anthropometrics from 514 Hmong and Karen individuals living in Thailand and the United States, including first- and second-generation immigrants and 19 Karen individuals sampled before and after immigration, as well as from 36 US-born European American individuals. Using 16S and deep shotgun metagenomic DNA sequencing, we found that migration from a non-Western country to the United States is associated with immediate loss of gut microbiome diversity and function in which US-associated strains and functions displace native strains and functions. These effects increase with duration of US residence and are compounded by obesity and across generations.

The gut microbiome plays a critical role in host metabolism and is heavily influenced by an individual’s long-term diet () and can also respond quickly to dramatic dietary changes (). Thus, the gut microbiome offers an important window into the consequences of diet and lifestyle changes associated with human migration. To study the short- and long-term impacts of migration on the microbiome, we measured gut microbiomes and dietary intake from Hmong and Karen immigrants and refugees (henceforth referred to as immigrants) in cross-sectional and longitudinal cohorts undergoing relocation to the United States stratified by BMI (high ≥ 25 and low < 25). A first-generation immigrant group (foreign-born US residents) included individuals with duration of US residence ranging from a few days to more than 40 years, allowing us to test for changes in the gut microbiome associated with long-term US residence. We included second-generation Hmong immigrants (born in the United States to first-generation Hmong immigrants) to determine whether the effects of US immigration were compounded across generations by birth in the United States. Finally, we followed a unique longitudinal cohort of 19 Karen refugees for up to 9 months beginning immediately before or after arrival in the Unites States to measure the short-term effects of US immigration.

The United States hosts the largest number of immigrants in the world (49.8 million or 19% of the world’s total immigrants and approximately 21% of the US population) (). Epidemiological evidence has shown that residency in the United States increases the risk of obesity and other chronic diseases among immigrants relative to individuals of the same ethnicity that continue to reside in their country of birth, with some groups experiencing up to a 4-fold increase in obesity after 15 years (). Refugees, in particular, appear to be more vulnerable to rapid weight gain (), with Southeast Asian refugees exhibiting the highest average increases in body mass index (BMI) after relocation to the United States (). The Hmong, a minority ethnic group from China who also reside in Southeast Asia, make up the largest refugee group in Minnesota (22,033 total refugees as of 2014) (). The Karen, an ethnic minority from Burma, have been arriving in large numbers in more recent years (). Overweight status and obesity rates are highest among Hmong and Karen compared to other Asian ethnic groups in Minnesota (). Western diet acculturation, previous exposure to food insecurity, and physical inactivity have been identified as contributing factors (), although they do not fully explain risk of obesity.

Previous work has established that diet and geographical environment are two principal determinants of microbiome structure and function (). Rural indigenous populations have been found to harbor substantial biodiversity in their gut microbiomes, including novel microbial taxa not found in industrialized populations (). This loss of indigenous microbes or “disappearing microbiota” () may be critical in explaining the rise of chronic diseases in the modern world. Despite the frequent migration of people across national borders in an increasingly interconnected world, little is known about how human migration affects the human microbiome.

This longitudinal cohort also included six Karen participants from whom we collected samples in Thailand, prior to their relocation to the United States. Using deep shotgun metagenomics sequencing on 13 samples from these 6 participants, we found that Prevotella and Bacteroides strain profiles remained largely stable over 6 months but sometimes underwent substantial changes (subject highlighted in blue, Figure 7 E). We observed in general that relocating to the United States induced a variety of short-term gut microbiome responses, including disruption to the gut microbiome immediately after arrival in two subjects (ID.273 and ID.304), expansion of opportunistic pathogens (ID.305), gut disruption several months after arrival (ID.275), and stability (ID.274, ID.308) ( Figure 7 F). Thus, we found that short-term responses to immigration of overall microbiome composition were variable across individuals, but the displacement of dominant native taxa with dominant US taxa begins within 6 to 9 months of US residence.

To understand whether changes in the gut microbiome can be detected immediately after relocation to the United States, we examined the gut microbiomes of 19 newly arrived Karen over their first 6–9 months of US residence. Within this short time frame, all but one participant gained weight (paired t test, p = 8.3 × 10 Figure 7 A), and protein consumption increased (paired t test, FDR-adjusted q = 0.048; Figure 7 B), while the total variety of foods consumed decreased (paired t test, p = 0.017; Figure 7 C), suggesting a period of acclimation to newly available foods. This is in contrast to the Hmong1st group, whose diet diversity tended to increase following US immigration ( Figure S7 B). Hmong participants’ diets may be more acculturated due to the longer duration of residence of the Hmong community (mean time US residence = 20.2 years among study participants) when compared to the Karen community (mean time US residence = 1.9 years among study participants). Hmong1st diets tend to be more similar on average than the Karen1st diets to the European American diets (t test of mean tree-based diet distance to European American group, p = 0.028). We again observed the displacement of Prevotella by Bacteroides (paired t test, p = 0.0013; Figure 7 D) within this longitudinal cohort, in many cases involving a 10-fold increase in the Bacteroides-Prevotella ratio, indicating that microbiome Westernization begins immediately after arrival to the United States.

(A) First and last month samples are highlighted and connected by participant, with all intermediate monthly samples in gray. Inset shows the within-individual changes along PC1 and PC2 from first to last months (one sample t test, PC1 p = 0.023, PC2 p = 0.35).

(F) Taxonomic area charts of relative abundances of dominant genera (other taxa not shown) in six individuals who began the longitudinal study while in a refugee camp in Thailand and then continued after relocation to the United States. First available samples were collected 6 to 34 days before departure, and second samples were collected 1 to 6 days after arrival to the United States.

(E) Bacteroides and Prevotella strain profiles are mostly stable after 6 months. Samples (columns) from the same participant are denoted by color, and M1 and M6 correspond to month 1 sample and month 6 sample, respectively. Selected strains are identical to Figure 3 B (at least 50% coverage per sample across n = 55 samples; see Table S5 ).

(A–D) (A) Comparison of per-participant changes between first and last months of the study in BMI (paired t test, p = 8.3 × 10 −05 ), (B) protein consumption (paired t test, macronutrients adjusted for multiple comparisons using FDR < 0.05, p = 0.048), (C) dietary diversity (Faith’s PD) (paired t test, p = 0.017), and (D) Bacteroides-to-Prevotella ratios (paired t test, p = 0.0013).

The longer immigrants spend living in the United States, the more their microbiome compositions diverge from their Thai counterparts and converge toward European Americans (Spearman correlation, ρ = −0.41, p = 1.3 × 10and ρ = 0.35, p = 1.2 × 10, respectively; Figure 6 A), with continued displacement of Prevotella with Bacteroides (Spearman’s correlation, ρ = 0.44, p = 8.76 × 10 Figure 6 B) over time. We confirmed that this significant association persisted after stratifying the first-generation immigrants by ethnicity, despite the shorter time frame of US residence in first-generation Karen (Spearman’s correlation, Hmong ρ = 0.47, p = 8.16 × 10; Karen ρ = 0.19, p = 0.023; Figure 6 B, inset). As in the case of diversity above, we note that age is highly correlated with years in the United States. However, we used the European American group as a control group to test for association of age with similarity to the HmongThai reference group and found no association (p = 0.57) ( Figure S6 A), and the B/P ratio in first-generation immigrants was significantly associated with years in the United States regardless of age (multiple linear regression, years in United States β = 0.096, p = 0.0094; age β = 0.039, p = 0.0065) ( Figure S6 B). These findings show that changes to the dominant members of the gut microbiome begin during the first decade of US residence and continue for multiple decades.

(B) Scatterplot of years in U.S. against Age in Hmong1st, colored by Bacteroides-Prevotella ratio. Years in U.S. was significantly associated with the B-P ratio in a multiple linear regression (p = 0.0094) while Age was not (p = 0.065).

(B) Log ratio of Bacteroides to Prevotella of first-generation groups is significantly correlated to years spent in the United States (Spearman’s correlation, ρ = 0.44, p = 8.76 × 10). Significantly correlated trends persist after stratification by ethnicity (Spearman’s correlation, Hmong ρ = 0.47, p = 8.16 × 10; Karen ρ = 0.19, p = 0.023). HT, HmongThai; KT, KarenThai; H2, Hmong2nd; C, controls; 0–40 = years spent in the United States by Hmong1st and Karen1st) (gray area represents 95% confidence interval). See also Figure S6 B.

After finding that US residence was associated with a major shift in dominant taxa in the microbiome ( Figure 3 A), we decided to test whether US residents experienced more profound changes in microbiome composition the longer they lived in the United States. In a PCoA of unweighted UniFrac microbiome-based distances, we found that time spent in the United States was strongly correlated with the first principal coordinate axis (⍴ = 0.62, p < 2.2 × 10 Figure 5 A). Conversely, gut biodiversity, as measured by Faith’s phylogenetic diversity, was negatively correlated with PC1 (⍴ = −0.34, p < 3.19 × 10 Figure 5 B), even while controlling for BMI in the Hmong (multiple linear regression, years in United States β = −0.18, p = 0.0275; Figure 5 C). We note that age was highly correlated with years in the United States in the Hmong1st group (Pearson correlation, ρ = 0.444, p = 4.5 × 10), and therefore, age is also strongly correlated with loss of diversity (multiple linear regression, age β = –0.38, p = 2.2 × 10). Age was not significantly correlated with diversity in any of the other groups (Pearson correlation, HmongThai p = 0.065, Hmong2nd p = 0.79, control p = 0.37). Thus, we found evidence that increased duration of US residence is associated with decreased microbiome diversity, but further study is needed to separate the effects of age and duration of US residence.

(C) In first-generation Hmong, diversity significantly decreases over time in the United States (multiple regression: years in United States β = −0.18, p = 0.0275; BMI β = −0.05, p = 0.81), but a significant association was not observed in first-generation Karen (years in United States β = −0.17, p = 0.71; BMI β = −0.27, p = 0.28) (gray area represents 95% confidence interval).

(A) Unweighted UniFrac PCoA of gut microbiomes of first-generation Hmong and Karen participants (n = 281), colored by years spent in the Unites States, which ranges from 1 day to 40.6 years. PC1 is strongly correlated with the amount of time spent in the United States (⍴ = 0.62, p < 2.2 × 10 −16 ).

Overall dietary profile was significantly associated with overall microbiome profile across individuals (Procrustes test, p = 0.001, n = 999 permutations) ( Figure S5 C), but constrained ordination of the microbiome by the first five principal coordinates of diet variation revealed that diet explained a relatively small fraction (16.8%) of the total variation explained in the microbiome PCoA ( Figure 4 C). Thus, we found that diet likely was not the sole contributor to the observed gut microbiome changes in our cohort, although it is possible that dietary variation explains substantially more microbiome variation in this cohort than we are able to determine due to our limited knowledge of precise polysaccharide and other nutrient content of the foods and due to complex individualized diet-microbiome interactions.

We observed significant differences across study groups in the consumption of macronutrients commonly associated with a Western diet: sugars, fats, and protein (unbalanced two-way ANOVA, p < 0.01; Figures 4 A and S4 ). There were no significant associations between fiber content and the microbiome, possibly due to the many uncharacterized polysaccharides present in different foods. PCoA of diet-based unweighted UniFrac () revealed distinct separation by sample group (ANOSIM R = 0.29, p = 0.001; Figure 4 B) and a gradient of dietary acculturation along PC1 ( Table S6 ). First- and second-generation Hmong had similar food choice profiles ( Figure 4 B), while US controls shared few foods with other groups and consumed almost 10-fold less white rice than other groups ( Figure S5 ). Although the microbiomes of US control and second-generation Hmong clustered together ( Figure 2 A), their diets did not ( Figure 4 B).

(C) Procrustes permutation shows significant relatedness between individuals’ food and microbiome profiles. Procrustes PCoA for a representative permutation (median of 9) and the original data (left), and a boxplot of distances between each individuals’ food and microbiome data in the original and permuted data after Procrustes rotation (distances are smaller in original data, Mann Whitney U test p = 1e-10).

(B) We highlight the high prevalence of rice consumption. Participants who consumed rice are denoted as yellow nodes and yellow edges connected to the centroid (rice), otherwise participants were colored by sample group.

(C) Redundancy analysis (RDA) of the unweighted UniFrac microbiome-distances constrained by the first five principal coordinates of the PCoA of unweighted UniFrac food distances. The resulting RDA explains 16.8% of the total variation explained by PC1 and PC2 of the microbiome PCoA ( Figure 2 A). See also Figure S5

(B) PCoA of unweighted UniFrac diet-based distances reveals significant clustering by sample group (ANOSIM R = 0.29, p = 0.001). Dietary acculturation can be seen along PC1 with Thai-resident groups on the left and European controls on the right.

(A) Comparison of macronutrient consumption across sample groups. Ethnicity is significantly associated with calories (p = 3.4 × 10), sugars (p = 0.00023), fat (p = 1.3e−07), and protein (p = 3.2 × 10), whereas US residency is associated with sugar (p = 1.3 × 10), fat (p = 7.1 × 10), and protein consumption (p = 5.7 × 10), and birth continent is only associated with fat consumption (p = 0.0081) (unbalanced two-way ANOVA). HT, HmongThai; KT, KarenThai; H1, Hmong1st; K1, Karen1st; H2, Hmong2nd; C, Controls. See also Figure S4

These three glycoside hydrolases predominantly originated from P. copri (42 ± 11.1%; Figure S3 B), supporting the hypothesis that loss of Prevotella strains following US immigration drove loss of plant-fiber degradation capability. We also observed a loss of GH5 and GH26 glycoside hydrolases from HmongThai to Hmong1st and US controls, which indicates a loss of cellulose and β-mannan and possible xyloglucan degradative potential. Beta-mannans are present in seeds, kernels, and corms, such as palm (), coconut (), and konjac (), and xyloglucan is found most abundantly in tamarind (); these, interestingly, are food ingredients prevalent in Southeast Asia. The loss of glycoside hydrolases for degrading cellulose, a plant cell-wall component, was another indication that the microbiota of post-immigration individuals had lost some of their ability to degrade plant-derived fibers (). These findings parallel previous findings in a mouse model demonstrating that the microbiomes of mice deprived of dietary fiber lost the capability to produce certain glycoside hydrolases ().

We identified differences in functional pathways () between HmongThai and long-term US-resident Hmong1st (>30 years residence) using shotgun metagenomics data (ANOVA, FDR-corrected q < 0.10; Figure S3 A). First-generation Hmong harbored microbiomes with increased capacity for sucrose degradation, glycerol degradation, glucose/xylose degradation, and glucose fermentation to lactate, potentially related to increased consumption of more sugary foods, although most sucrose and glucose would not be expected to reach the lower gastrointestinal (GI) tract (). In HmongThai, we found an enrichment of pathways relating to the degradation of complex carbohydrates, including β-(1,4)-mannan degradation and starch degradation (). In order to better understand the substrates degraded by these pathways that are either lost or below the detection limit in US immigrants, we assembled the shotgun data into scaffolds and annotated carbohydrate-degrading enzymes (CAZymes) (). We found significant shifts in abundance of 58 CAZymes across the HmongThai, Hmong1st, and control groups (Kruskal-Wallis test, FDR-corrected q < 0.05; Figure 3 C), including three β-glucan-targeting glycoside hydrolases (GH17, GH64, and GH87) that were highly abundant in the Thailand group but almost entirely unobserved in the US groups. Loss of these glycoside hydrolases may be associated with loss of dietary fiber sources that promote persistence of the organisms that harbor these enzymes, reducing the ability of the microbiota to degrade these dietary fibers.

Using deep shotgun metagenomics on 55 samples (mean 22,406,875 reads per sample) from Hmong in Thailand, newly arrived Karen, long-term (>30 years) US resident Hmong, and controls, we profiled strain-level variation within Bacteroides and Prevotella. We aligned shotgun metagenomic sequences against all 256 Bacteroides genomes and 153 Prevotella genomes in RefSeq version 87 (), retaining any strains with at least 50% genome coverage in at least one sample. We found that US controls had varied Bacteroides strain profiles, while those with Prevotella had only a single strain of P. copri ( Figure 3 B). Conversely, Thailand-based individuals carried up to four strains of Prevotella, with low abundance and generally low genomic coverage of Bacteroides strains possibly due to lack of related strains in the database. Long-term US-resident Hmong displayed an intermediate profile, carrying a variety of Bacteroides strains and, in several individuals, multiple Prevotella strains. Prevalence-abundance curves for the Bacteroides and Prevotella strains with the largest change in overall prevalence between HmongThai and Hmong1st showed marked loss of Prevotella strains accompanied by an expansion of pre-existing low-abundance Bacteroides strains following US immigration ( Figure S2 B).

The Western-associated genus Bacteroides increasingly displaced the non-Western-associated genus Prevotella across generations in the United States ( Figure 3 A). The ratio of Bacteroides to Prevotella was lowest in Thailand-resident individuals, highest in US-born European Americans, and increased in a stepwise fashion from first-generation Karen to first-generation Hmong to second-generation Hmong (unbalanced two-way ANOVA, resident continent p = 3.4 × 10, birth continent p = 0.00085, ethnicity p = 5.5 × 10). This progression corresponded with the time that these groups had spent in the United States.

(C) CAZymes with significantly different relative abundances across HmongThai, Hmong1st (who have lived in the United States for more than 30 years), and controls (Kruskal-Wallis test, FDR-corrected q < 0.05). See also Figure S3

(B) Coverage and relative abundance of Bacteroides and Prevotella strains in 44 samples across HmongThai, Hmong1st (who have lived in the United States for more than 30 years), and controls. Strains with genomic coverage >50% in at least one sample were included. Hierarchical clustering of strains and samples within group is based on relative abundances. Strains with genome coverage of <1% within a person are considered not present (not plotted). See Table S5 for strain names.

(A) Log-transformed ratio of Bacteroides to Prevotella (B/P) relative abundances. US residence, US birth, and ethnicity were all significantly associated with B/P ratio (unbalanced two-way ANOVA, p = 3.4 × 10 −13 , p = 0.00085, p = 5.5 × 10 −12 , respectively). KT, KarenThai; HT, HmongThai; K1, Karen1st; H1, Hmong1st; H2, Hmong2nd; C, controls.

Microbial diversity and richness were highest in Thailand and decreased with each generation of residence in the United States (Tukey’s honest significant difference [HSD], p < 0.01; Figure 2 B). As in other studies (), we found that lower phylogenetic diversity was associated with obesity across all major study groups (unbalanced two-way ANOVA, p = 0.0044; Figure 2 B), even after stratification by ethnicity (Tukey’s HSD, p < 0.01; Figure S2 A). Furthermore, we observed a consistent loss of certain native bacterial operational taxonomic units (OTUs) among first-generation Hmong ( Figure 2 C). Although 7 of the 10 most prevalent OTUs found in HmongThai were also found at similar levels in Hmong1st, others such as otu1812 (Faecalibacterium prausnitzii) incurred a 45% loss in prevalence (Fisher’s exact test, false discovery rate [FDR]-corrected q = 3.05 × 10) ( Table S4 ). Prevalence-abundance curve analysis showed that many OTUs that were highly prevalent (>75% prevalence) in Thai-resident individuals had both decreased abundance and prevalence in first-generation US residents (paired t test, area under the prevalence-log-abundance curve, HmongThai versus Hmong1st, p < 2.2 × 10) ( Figure 2 D). 28 OTUs incurred at least a 50% loss in prevalence among first-generation Hmong, with more than half of them belonging to the genus Prevotella ( Table S4 ).

We performed amplicon-based sequencing of the 16S rRNA gene V4 region on 550 stool samples (one sample per participant). Principal-coordinates analysis (PCoA) of unweighted UniFrac distances () revealed that Hmong and Karen harbor distinct gut microbial compositions regardless of country of residence, yet their microbiomes converge toward European American microbiomes after relocating to the United States (analysis of similarities [ANOSIM] R = 0.25, p = 0.001), with second-generation Hmong and European American microbiomes sharing nearly identical cluster centroids ( Figure 2 A). Interestingly, all US immigrant groups had higher interindividual variation than their Thai counterparts (t test Hmong1st versus HmongThai, p = 1.2 × 10; Hmong1st versus HmongThai, p = 6.5 × 10; Karen1st versus KarenThai, p = 4.9 × 10). The first-generation immigrants with the most perturbed microbiomes (most distant tertile from Thai groups) had both higher age (t test, p = 0.0013) and longer time (t test, p = 0.00079) in the United States than those with the least perturbed microbiomes (least distant tertile from Thai groups).

(D) Prevalence-abundance curves of all OTUs present in at least 75% of HmongThai samples, plotted separately for the Hmong1st and HmongThai sample groups. See also Figure S2 B.

(C) Prevalence of OTUs in HmongThai and Hmong1st, with OTUs sorted by prevalence in HmongThai and samples sorted by richness within sample group. OTUs shown are found in at least 75% of HmongThai samples (see Table S4 for taxonomic assignments, mean group prevalence, and statistics).

(B) Alpha diversity of obese and lean individuals across sample groups in Shannon’s diversity index and Faith’s phylogenetic distance (PD). P values denote significantly different groups using pairwise tests of sample groups without stratification by BMI (Tukey’s HSD, p < 0.01). Microbiome diversity is significantly lower in obese individuals across all sample groups (unbalanced two-way ANOVA analysis with BMI class and sample group as covariates, p = 0.0044). See also Figure S2 A.

(A) PCoA of unweighted UniFrac distances between bacterial communities of cross-sectional participants revealed that phylogenetic variation was differentiated by sample group (ANOSIM R = 0.25, p = 0.001). 95% standard error ellipses are shown around Hmong and Karen in Thailand, second-generation Hmong, and controls.

To be able to associate gut microbiome variation with dietary intake, we collected 24-hr dietary recalls from all participants and analyzed macronutrient content using the United States Department of Agriculture (USDA) SuperTracker food record system () and published literature. We utilized the hierarchical format of food codes derived from the USDA’s Food Nutrient and Database for Dietary Studies (FNDDS) to categorize foods into a tree structure where more closely related foods were grouped together ( Figure 1 C). These groupings allowed us to share statistical strength across closely related foods to complement dietary analysis of macronutrients, much in the way that phylogenetic beta-diversity analysis complements taxonomy-based profiles of microbiomes. Foods reported by participants that were not found in any USDA database (n = 72; Table S3 ) were manually inserted into the hierarchical food tree, allowing us to account for all foods reported by all participants. We confirmed our ability to discriminate between the Karen1st, Hmong1st, and Hmong2nd group diets using tree-based distances ( Figure S1 B), identifying a stark increase in the variety of foods eaten by second-generation Hmong relative to Hmong in Thailand ( Figure 1 C) (t test of phylogenetic diversity, p = 4.828 × 10).

Bilingual-bicultural research teams collected migration and medical histories ( Table S2 ), anthropometrics (weight, height, waist circumference), 24-hr dietary recalls, and single stool samples from all participants. Karen participants who were about to leave Thailand for the United States or who had arrived in the United States within 2 months were invited to participate in a longitudinal sub-study in which 24-hr dietary recalls and stool samples were collected monthly for 6 months ( Figure 1 A). We collected a total of 673 stool samples comprising 531 single- and 142 multiple-time-point collections. Consistent with the previously observed high rate of obesity in US immigrants (see Introduction ), obesity prevalence relative to overweight status in our cohort increased after a decade in the United States in the Hmong1st group (Chi-squared test statistic = 5.23, p = 0.022) ( Figure 1 B). There was not a sufficient number of Karen subjects with long-term US residence to test for changes in prevalence of obesity.

We recruited 514 healthy Hmong and Karen female individuals (aged 18–78; see STAR Methods for full exclusion criteria) who either (1) were living in Thailand (HmongThai, KarenThai; n = 179), (2) were born in Southeast Asia and had moved to the United States (Hmong1st, Karen1st; n = 281), or (3) were born in the United States and whose parents were born in Southeast Asia (Hmong2nd; n = 54) ( Figure 1 A). We also recruited healthy European American female individuals to serve as US controls (controls; n = 36) ( Figure 1 A). We limited our study to women based on insight from our Hmong community advisory board that substantially more Hmong women than men were relocating to United States. Participants in each sample group were recruited into lean or overweight/obese BMI class stratifications (BMI < 25 or BMI ≥ 25, respectively) ( Table S1 ). In 2016 and 2017, we recruited eligible individuals throughout the Minneapolis-St. Paul metropolitan area in Minnesota and at two locations in Thailand: a rural village in Chiang Mai province (Khun Chang Khian) and a refugee camp in Tak province (Mae La) ( Figure S1 A).

(B) Principal coordinates analysis of tree-based unweighted UniFrac diet distances between Karen1st and Hmong1st (left) (Adonis F statistic = 43.85, p < 0.001) and between Hmong1st and Hmong2nd (right) (Adonis F statistic = 13.05, p < 0.001), showing the ability of the method to discriminate between these immigrant groups.

(C) HmongThai (n = 43) and Hmong2nd (n = 41) (ages 20–40) diet diversity displayed on a tree that groups related foods together. Bars denote unique foods, with darkness of the bar showing prevalence of foods reported averaged within HmongThai or Hmong2nd. Items highlighted in red denote the most prevalent vegetables, sweets and beverages, grains, and meats reported within sample groups. Full descriptions of foods highlighted in red: coffee, brewed, regular; carbonated citrus fruit drink; Chinese cabbage or bok choy family, raw; rice, white, no salt or fat added; pork chop, broiled, baked, or grilled, lean only eaten; chicken breast, roasted, skin not eaten. See also Figure S1 B.

Discussion

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Falkow S. What are the consequences of the disappearing human microbiota?. This article reports the first large-cohort study of the effects of migration from a non-Western country to a Western country on the human gut microbiome. In multi-ethnic, multi-generational cohorts of immigrants and refugees, we observed that gut microbiome diversity, function, and strain composition are strongly impacted by US immigration and that both short-term and long-term US residence and being born in the United States shift an individual’s microbiome along an axis toward a more Westernized state. Even a short period of residence in the United States was sufficient to induce pronounced increases, in some cases over 10-fold, in the ratio of Bacteroides to Prevotella. Metagenome assembly showed that the observed loss of Prevotella strains was associated with loss of carbohydrate-active enzymes dominant in the gut microbiota, including a near-complete loss of certain β-glucanases and other glycoside hydrolases that break down specific dietary fibers. Previous studies have demonstrated intergenerational effects of microbiome perturbations in animal models. These include loss of microbiota carbohydrate degradation function following removal of dietary fiber () and intergenerational loss of diversity following antibiotic perturbation (). The data presented here extend these findings to humans by providing evidence that compounded intergenerational loss of taxonomic and functional diversity is occurring in US immigrant populations, supporting the model of disappearing human microbiota proposed by

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Gordon J.I. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. We also performed extensive analysis and modeling of differences in dietary intake, as diet is known to be a strong driver of microbiome variation (). Although we observed clear patterns of dietary acculturation associated with US residence, dietary variation only partly explained microbiome variation across individuals. Interestingly, the diets of second-generation immigrants remained quite distinct from the controls, while their microbiomes did not. It is possible that different diets are driving the microbiome toward a similar state; an alternative explanation is that a limited set of metabolic capabilities in the microbiome are transmitted from one generation to the next, resulting in decreased overall functionality with each successive generation, consistent with the disappearing microbiota model.

This study has several limitations. Immigration-related microbiome changes are likely driven by a combination of diet and other factors associated with adjustment to life in the United States, and most of these factors were not examined in the context of this study. These include changes in exposure to stress, exercise, municipal drinking water, antibiotics, and treatment with antiparasitics. In addition, our study design did not allow us to test directly whether immigration causes the observed changes in the microbiome nor whether changes in microbiome are directly contributing to the high incidence of obesity in US immigrants.

Our findings demonstrate that US immigration is associated with profound perturbations to the gut microbiome, including loss of diversity, loss of native strains, loss of fiber degradation capability, and shifts from Prevotella dominance to Bacteroides dominance. These changes begin immediately upon arrival, continue over decades of US residence, and are compounded in obese individuals and in second-generation immigrants born in the United States. These results improve our fundamental understanding of how migration affects the human microbiome and underscore the importance of considering the impact of the gut microbiome in future research into immigrant and refugee health.