Gut microbiome in Hmong/Karen immigrants and US residents
Cross-sectional study of 55 samples from individuals in the USA, part of a larger project examining how US immigration affects gut microbiome diversity in Hmong and Karen populations compared to European Americans. machine-written summary
55 samples · 55 runs · US · Adult · 2018 · 5.60 Gbp / sample
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ✓ Sequence archive | 297 sample values (collection date, country, latitude, location region, longitude, subject id, timepoint) |
| ○ Abstract / description | read; no usable statement |
| ○ Full text | read; no usable statement or table · PMC10165778, PMC10201371, PMC10913632, PMC6498444 |
| ○ Supplementary tables | files read; no table could be matched to the samples |
| – External resources | not covered by the external resources we ingest |
| – User contribution | nothing uploaded yet · share metadata |
| – Human verified | not yet checked by a person · I checked this project — verify it |
intervention — arms not resolved
design observational_with_procedure
longitudinal
5 of 6 people sampled more than once (10 samples; median 2, up to 2 per person) · follow-up median 5 mo (max 5 mo) · median interval between samples 5 mo (time from timepoint labels). Schedule stated by the source: “stool sampled before and after immigration (no fixed interval stated)” (time origin: other).
Each row is one person (5 shown); dots are samples at days since that person's first sample.
Genera, mean relative abundance over 47 profiled gut samples of this project (Sandpiper SingleM profiles, GTDB R232; low-depth and non-metagenome profiles left out).
| field | samples | coverage | R1 | R2 | R3 | R4 |
|---|---|---|---|---|---|---|
| age_category | 55 | 100% | ||||
| country | 55 | 100% | 55 | |||
| health_condition | 0 | 0% | ||||
| subject_id | 11 | 20% | 11 | |||
| collection_date | 55 | 100% | 55 | |||
| detailed_location | 55 | 100% | ||||
| latitude | 55 | 100% | 55 | |||
| location_region | 55 | 100% | 55 | |||
| longitude | 55 | 100% | 55 | |||
| timepoint_label | 11 | 20% | 11 |
- US Immigration Westernizes the Human Gut Microbiome. 2018 PMID 30388453 PMC6498444 own_data
- Reconstructing the landscape of gut microbial species across 29,000 diverse individuals. 2023 PMID 37070603 PMC10201371 own_data
- Gut microbiome transitions across generations in different ethnicities in an urban setting-the HELIUS study. 2023 PMID 37158898 PMC10165778 own_data
- Proportion-based normalizations outperform compositional data transformations in machine learning applications 2024 PMC10913632 unsure
Archive description
Many United States 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 U.S. immigration on the gut microbiome. We collected stool, dietary recalls, and anthropometrics from 514 Hmong and Karen individuals living in Thailand and the U.S., including first- and second-generation immigrants and 19 Karen individuals sampled before and after immigration, as well as from 36 U.S.-born European American individuals. Using 16S and deep shotgun metagenomic DNA sequencing, we found that migration from a non-Western country to the U.S. is associated with immediate loss of gut microbiome diversity and function, in which U.S.-associated strains and functions displace native strains and functions. These effects increase with duration of U.S. residence, and are compounded by obesity and across generations.
How this project entered the registry
Classified as human gut / stool shotgun DNA metagenome of adult by deterministic_prior with confidence 0.90. Assay shotgun_dna.
- host external_curation.infant_catalog · “infant triage reason_code age_adult_only”
- body site study.title · “westernizes the human gut microbiome”
- body site study.description · “immigration on the gut microbiome. We collected”
- life stage external_curation.infant_catalog · “infant triage reason_code age_adult_only”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMEA4973145 | collection_date | 2016 | R1 | 0.90 | biosample.attribute:collection_date | 2016 |
| SAMEA4973145 | country | US | R1 | 0.90 | sample.attr.geographic_location_country_and_or_sea | USA |
| SAMEA4973145 | latitude | 44.9778 | R1 | 0.90 | biosample.attribute:geographic_location_latitude+geographic_location_longitude | 44.9778� N 93.2650� W |
| SAMEA4973145 | location_region | Minnesota | R1 | 0.60 | biosample.attribute:geographic_location_latitude+geographic_location_longitude | 44.9778� N 93.2650� W |
| SAMEA4973145 | longitude | -93.2650 | R1 | 0.90 | biosample.attribute:geographic_location_latitude+geographic_location_longitude | 44.9778� N 93.2650� W |
| SAMEA4973174 | subject_id | 002 | R1 | 0.70 | sample.name_rule:sample_alias | wgs.L_002_M1 |
| SAMEA4973174 | timepoint_label | M1 | R1 | 0.70 | sample.name_rule:sample_alias | wgs.L_002_M1 |
Sequencing
55 runs over 55 samples · mean 5.61 Gbp per run (median 5.60 Gbp, total 308.33 Gbp; 55 runs with a base count) · layout PAIRED 55 · platform ILLUMINA 55 · models Illumina HiSeq 2500 (55) · 85% of runs with a Sandpiper profile · first public 2018-10-31
Authors (53)
Vangay P first · Johnson AJ · Ward TL · Al-Ghalith GA · Shields-Cutler RR · Hillmann BM · Lucas SK · Beura LK · Thompson EA · Till LM · Batres R · Paw B · Pergament SL · Saenyakul P · Xiong M · Kim AD · Kim G · Masopust D · Martens EC · Angkurawaranon C · McGready R · Kashyap PC · Culhane-Pera KA · Knights D last · Tarracchini C first · Fontana F · Mancabelli L · Lugli GA · Alessandri G · Turroni F · Ventura M · Milani C last · Segota I first · Watrous JD · Kantz ED · Nallamshetty S · Tiwari S · Cheng S · Jain M · Long T last · van der Vossen EWJ first · Davids M · Bresser LRF · Galenkamp H · van den Born BH · Zwinderman AH · Levin E · Nieuwdorp M · de Goffau MC last · van Sinderen D · Yerke A first · Fry Brumit D · Fodor A last
Help complete this project
2 of the 4 core fields are below 50% coverage: health_condition, subject_id. Share metadata
Downloads
PRJEB28687.csv.gz (2.2 KB) · parquet (71.6 KB) — all 55 samples, every field with route and confidence · PRJEB28687_determinations.csv.gz (2.3 KB) — 297 evidence rows