The Integrative Human Microbiome Project
Open accessAbstract
The NIH Human Microbiome Project (HMP) has been carried out over ten years and two phases to provide resources, methods, and discoveries that link interactions between humans and their microbiomes to health-related outcomes. The recently completed second phase, the Integrative Human Microbiome Project, comprised studies of dynamic changes in the microbiome and host under three conditions: pregnancy and preterm birth; inflammatory bowel diseases; and stressors that affect individuals with prediabetes. The associated research begins to elucidate mechanisms of host–microbiome interactions under these conditions, provides unique data resources (at the HMP Data Coordination Center), and represents a paradigm for future multi-omic studies of the human microbiome.
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Written from the paper’s full text.
TL;DR
The Integrative Human Microbiome Project (iHMP/HMP2) reports three longitudinal multi-omic cohort studies linking host and microbial dynamics to pregnancy/preterm birth, inflammatory bowel disease, and prediabetes. It also describes the resulting data resources and argues that longitudinal, multi-omic profiling is needed to capture personalized host–microbiome interactions.
Key findings
- In pregnancy/preterm birth (MOMS-PI), 1,527 women were followed and 206,437 specimens collected; analysis of 12,039 samples from 597 pregnancies found that women who had spontaneous preterm birth were less likely to have *Lactobacillus crispatus*-dominated vaginal microbiota and more likely to have increased *Sneathia amnii*, *Prevotella*-related clades, BVAB1, and TM7-H1, with prediction most sensitive/specific using samples before 24 weeks.
- In IBD (IBDMDB), 132 individuals from five clinical centres were followed for one year each, with 1,785 stool samples, 651 intestinal biopsies, and 529 quarterly blood samples; longitudinal changes were larger than cross-sectional differences among clinical phenotypes, and a network of more than 2,900 significant host and microbial interactors was constructed.
- In prediabetes (IPOP), 106 healthy/prediabetic individuals were followed over about four years across 1,092 collections; insulin-resistant and insulin-sensitive participants differed at baseline and in response to respiratory viral infection or weight change, with insulin-resistant participants showing decreased/delayed inflammatory responses and altered gut microbial changes.
- Across HMP1 and HMP2, 42 terabytes of multi-omic data were produced and archived at the HMP Data Coordination Center, which receives 9,000–12,000 user sessions each month; shotgun metagenomics, untargeted metabolomics, and immunoprofiling were particularly effective, whereas genetic variants were generally difficult to link to the microbiome in these small deeply profiled populations.
Methods
- HMP2 comprised three longitudinal, multi-omic cohort studies: MOMS-PI for pregnancy and preterm birth, IBDMDB for inflammatory bowel diseases, and IPOP for prediabetes.
- MOMS-PI followed 1,527 pregnancies and collected 206,437 specimens, with subsets analyzed by 16S rRNA gene sequencing, metagenomics, metatranscriptomics, cytokine profiling, lipidomics, and bacterial genome analysis; 12,039 samples from 597 pregnancies were analyzed in depth.
- IBDMDB followed 132 individuals for one year each with stool, biopsy, and blood multi-omics; IPOP followed 106 individuals quarterly over about four years, profiling host exomes, transcriptomes, metabolomes, proteomes, cytokines, clinical labs, and microbial 16S profiles, including a 23-person weight gain/loss subset.
Limitations
- Stated: the HMP2 raised more questions than it answered; the aetiologies of baseline inter-individual microbiome differences and dynamic changes were not apparent, it remains unclear whether specific strains are sufficient or necessary for disease phenotypes, and the small molecules or immune cell subsets transmitting gut signals to systemic conditions were not identified.
- Stated: each HMP2 study was carried out within a geographically and genetically constrained population, and global differences in early life events, infectious disease exposure, or diet may change how microbiome dynamics contribute to disease.
- Evident: the three studies are separate condition-specific cohorts rather than one unified population, and the findings are largely longitudinal associations rather than direct causal demonstrations.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
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Claims the paper makes
0 of 44 cited sources checked · 0 supported · 0 flagged
- Findingnone given
Most women in MOMS-PI who experienced spontaneous PTB at less than 37 weeks of gestation were of African ancestry.
“Although the overall MOMS-PI cohort was demographically diverse, most women who experienced spontaneous PTB at less than 37 weeks of gestation were of African ancestry.”
Quote in the paperNot a substantial claim - Findingown data
A preliminary model to predict risk of PTB was most sensitive and specific using vaginal microbiome profiles from samples collected before 24 weeks of gestation.
“and a preliminary model to predict risk of PTB was most sensitive and specific using vaginal microbiome profiles from samples collected before 24 weeks of gestation.”
Quote in the paperSubstantial claim - Findingown data
Women who went on to experience spontaneous PTB were more likely to exhibit increased proportional abundance of several taxa including Sneathia amnii, Prevotella-related clades, BVAB1, and TM7-H1.
“and were more likely to exhibit an increased proportional abundance of several taxa including Sneathia amnii, Prevotella-related clades, a Lachnospiraceae taxon known as BVAB1, and a Saccharibacteria bacterium known as TM7-H1.”
Quote in the paperSubstantial claim - Findingnone given
PTB signatures were reflected in metagenomic and metatranscriptomic measurements.
“The signatures of PTB were also reflected in metagenomic and metatranscriptomic measurements”
Quote in the paperSubstantial claim - Findingown data
Vaginal pro-inflammatory cytokines including IL-1β, IL-6, MIP-1β and eotaxin-1 were positively correlated with PTB-associated taxa.
“and vaginal pro-inflammatory cytokines (including IL-1β, IL-6, MIP-1β and eotaxin-1) were positively correlated with PTB-associated taxa.”
Quote in the paperSubstantial claim
- Findingcitation
The PTB-associated vaginal taxa were also associated with low levels of vitamin D.
“Notably, these taxa were also associated with low levels of vitamin D63”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [63] Jefferson, K. K. et al. Relationship between vitamin D status and the vaginal microbiome during pregnancy. J. Perinatol. https://doi.org/10.1038/s41372-019-0343-8 (2019). source
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- Findingnone given
The convergence trend towards a Lactobacillus-dominated vaginal microbiome was most pronounced in women of African ancestry with lower socioeconomic profiles.
“Interestingly, this trend was most pronounced in women of African ancestry with lower socioeconomic profiles.”
Quote in the paperNot a substantial claim - Findingcitation
In MOMS-PI, women who began pregnancy with a vaginal microbiome of greater ecological complexity generally converged towards a more homogeneous Lactobacillus-dominated microbiome by the second trimester.
“Women who often began pregnancy with a vaginal microbiome of greater ecological complexity generally converged towards a more homogeneous Lactobacillus-dominated microbiome by the second trimester58.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [58] Serrano, M. G. et al. Racioethnic diversity in the dynamics of the vaginal microbiome during pregnancy. Nat. Med. https://doi.org/10.1038/s41591-019-0465-8 (2019). HMP2 term birth study of 613 pregnant women, 1,969 non-pregnant women, and an additional 90 pregnant women followed longitudinally profiling vaginal, buccal, blood, urine, and rectal samples; shifts toward Lactobacillus -dominated communities tended to occur early during pregnancy, particularly in women of African or Hispanic descent. source
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- Findingcitation
Women who went on to experience spontaneous PTB were less likely to exhibit a vaginal microbiota dominated by Lactobacillus crispatus.
“Women who went on to experience spontaneous PTB were less likely to exhibit a vaginal microbiota dominated by Lactobacillus crispatus, as previously reported in other populations59–62”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [59] DiGiulio, D. B. et al. Temporal and spatial variation of the human microbiota during pregnancy. Proc. Natl Acad. Sci. USA 112, 11060–11065 (2015). source
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- cites [60] Callahan, B. J. et al. Replication and refinement of a vaginal microbial signature of preterm birth in two racially distinct cohorts of US women. Proc. Natl Acad. Sci. USA 114, 9966–9971 (2017). source
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- cites [61] Kindinger, L. M. et al. The interaction between vaginal microbiota, cervical length, and vaginal progesterone treatment for preterm birth risk. Microbiome 5, 6 (2017). source
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- cites [62] Brown, R. G. et al. Vaginal dysbiosis increases risk of preterm fetal membrane rupture, neonatal sepsis and is exacerbated by erythromycin. BMC Med. 16, 9 (2018). source
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- Findingown data
The vaginal microbiomes of mothers who experienced PTB were most distinct from those of control mothers early in pregnancy.
“the vaginal microbiomes of mothers who experienced PTB were most distinct from those of control mothers early in pregnancy”
Quote in the paperSubstantial claim - Findingcitation
MOMS-PI identified signatures of higher risk for PTB in women who experienced spontaneous preterm birth at less than 37 weeks of gestation.
“The MOMS-PI team (http://vmc.vcu.edu/momspi) also identified signatures of higher risk for PTB in women who experienced spontaneous preterm birth at less than 37 weeks of gestation50.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [50] Fettweis, J. M. et al. The vaginal microbiome and preterm birth. Nat. Med. https://doi.org/10.1038/s41591-019-0450-2 (2019). HMP2 preterm birth study of 1,527 pregnancies focusing on 45 spontaneous preterm birth events and 90 case-matched controls, together yielding over 12,000 multi-omically profiled samples; 16S rRNA gene, metagenomic, metatranscriptomic and cytokine profiles identified differential levels of Lactobacillus crispatus, BVAB1, Sneathia amnii, TM7-H1, and additional taxa associated with preterm deliveries. source
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- Findingcitation
Baseline measurements were generally stable within individuals, even for long periods of time, with only some analytes changing significantly over time.
“Baseline measurements were generally stable within individuals, even for long periods of time, with only some analytes changing significantly over time51.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [51] Zhou, W. et al. Longitudinal multi-omics of host–microbe dynamics in prediabetes. Nature https://doi.org/10.1038/s41586-019-1236-x (2019). HMP2 prediabetes study of 106 participants spanning 1,092 visits, profiling host transcriptomes, metabolomes, cytokines, and proteomes, as well as microbial taxonomic and genomic changes during healthy periods, respiratory viral infections, immunizations, and other perturbations; most molecular profiles were personalized, perturbed during infections and immunizations, and associated with changes in insulin sensitivity. source
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- Findingcitation
Concurrent transcriptomics and 16S amplicon mucosal community profiling from biopsies identified potential host factors that might shape the microbial community, in particular several chemokines.
“Concurrent transcriptomics and 16S amplicon mucosal community profiling from biopsies also identified potential host factors that might be able to shape the microbial community, in particular several chemokines, highlighting these as being involved in a potentially dysregulated interaction during periods of disease activity49.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [49] Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature https://doi.org/10.1038/s41586-019-1237-9 (2019). HMP2 Inflammatory Bowel Disease Multi’omics Database (IBDMDB) study profiling 2,965 stool, biopsy, and blood specimens from 132 individuals with Crohn’s disease, ulcerative colitis or neither (control subjects) followed longitudinally for one year each; multi-omic profiling identified taxa, expressed functions, metabolite pools, and host gene expression that were disrupted in the gut during increased disease activity, as well as systemic eff source
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- Findingown data
The IBD network identified several key components central to alterations in IBD, including octanoyl carnitine, several lipids and short-chain fatty acids, the taxa Faecalibacterium, Subdoligranulum, Roseburia, Alistipes, and Escherichia, and host regulators of interleukins.
“This network of mechanistic associations identified several key components that are central to the alterations seen in IBD, highlighting octanoyl carnitine, several lipids and short-chain fatty acids, the taxa Faecalibacterium, Subdoligranulum, Roseburia, Alistipes, and Escherichia, some at both the metagenomic and metatranscriptomic levels, and host regulators of interleukins49.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [49] Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature https://doi.org/10.1038/s41586-019-1237-9 (2019). HMP2 Inflammatory Bowel Disease Multi’omics Database (IBDMDB) study profiling 2,965 stool, biopsy, and blood specimens from 132 individuals with Crohn’s disease, ulcerative colitis or neither (control subjects) followed longitudinally for one year each; multi-omic profiling identified taxa, expressed functions, metabolite pools, and host gene expression that were disrupted in the gut during increased disease activity, as well as systemic eff source
Not checked in the free run.
- Findingown data
Individuals undergoing RVI or changes in weight showed thousands of specific molecular and microbial changes during these perturbations.
“Notably, individuals undergoing RVI or changes in weight showed thousands of specific molecular and microbial changes during these perturbations”
Quote in the paperSubstantial claim - Findingown data
Dynamic changes in the microbiome and host immune response during IBD were of much greater magnitude than cross-sectional differences among clinical phenotypes.
“Indeed, these dynamic changes were of much greater magnitude than were cross-sectional differences among clinical phenotypes, which have been emphasized by previous studies67,71,78,79.”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [67] Frank, D. N. et al. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc. Natl Acad. Sci. USA 104, 13780–13785 (2007). source
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- cites [71] Morgan, X. C. et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 13, R79 (2012). source
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- cites [78] Willing, B. P. et al. A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology 139, 1844–1854.e1841 (2010). source
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- cites [79] Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014). source
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- Findingnone given
Changes that occurred within individuals, populations, or phenotypes were often much smaller than baseline variation between individuals.
“changes that occurred within individuals, populations, or phenotypes were often much smaller than baseline variation between individuals”
Quote in the paperSubstantial claim - Findingreasoning
Genetic variants were generally difficult to link to the microbiome in the small populations used for deep longitudinal multi-omic profiling.
“genetic variants were generally difficult to link to the microbiome in such small populations, which were necessary in order to deeply profile multi-omics over time”
Quote in the paperSubstantial claim - Findingnone given
A surprising range of host-microbiome immune and ecological features were common among the three HMP2 studies.
“a surprising range of host–microbiome immune and ecological features were common among them”
Quote in the paperSubstantial claim- Rests on an assumption: The three HMP2 studies' multi-omic measurements and populations are sufficiently comparable to allow identification of common host–microbiome features across them.
- Findingnone given
Many immune and biochemical responses appear to be associated with specific strains that are unique to one or a few individual hosts.
“Many immune and biochemical responses appear to be associated with specific strains that are unique to one or a few individual hosts”
Quote in the paperNot a substantial claim - Findingreasoning
In all three conditions, microbial changes and associated host responses were strongest when captured at the time the changes occurred, and often within the tissue of origin.
“In all three conditions, microbial changes and associated host responses were strongest when captured at the time the changes occurred, and often within the tissue of origin.”
Quote in the paperSubstantial claim - Findingnone given
The aetiologies of baseline inter-individual differences in the microbiome, and of its dynamic changes over time, were not apparent even from the wide range of measurement types incorporated into the three HMP2 studies and populations.
“The aetiologies of baseline inter-individual differences in the microbiome, and of its dynamic changes over time, were not apparent even from the wide range of measurement types incorporated into these three studies and populations.”
Quote in the paperSubstantial claim - Findingown data
Many analytes, such as clinical laboratory measurements, cytokine profiles, and gut microbial taxa mostly of low abundance, were highly variable between individuals.
“However, many analytes, such as clinical laboratory measurements, cytokine profiles, and gut microbial taxa (mostly those of low abundance) were highly variable between individuals.”
Quote in the paperNot a substantial claim - Findingown data
Insulin-resistant and insulin-sensitive individuals responded very differently to perturbations.
“and insulin-resistant and insulin-sensitive individuals responded very differently to perturbations.”
Quote in the paperSubstantial claim - Findingcitation
Global co-association analyses revealed specific associations in insulin-resistant individuals that differ from those seen in insulin-sensitive participants and vice versa.
“Furthermore, global co-association analyses among the thousands of profiled molecules revealed specific associations in insulin-resistant individuals that differ from those seen in insulin-sensitive participants and vice versa, indicating different patterns of host–microbiome interactions in the two groups51.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [51] Zhou, W. et al. Longitudinal multi-omics of host–microbe dynamics in prediabetes. Nature https://doi.org/10.1038/s41586-019-1236-x (2019). HMP2 prediabetes study of 106 participants spanning 1,092 visits, profiling host transcriptomes, metabolomes, cytokines, and proteomes, as well as microbial taxonomic and genomic changes during healthy periods, respiratory viral infections, immunizations, and other perturbations; most molecular profiles were personalized, perturbed during infections and immunizations, and associated with changes in insulin sensitivity. source
Not checked in the free run.
- Findingreasoning
Other aspects of host-microbiome interactions were highly localized and subject-specific within each of the three HMP2 studies.
“other aspects of host–microbiome interactions were highly localized and subject-specific within each of the three studies”
Quote in the paperSubstantial claim - Findingown data
The success of the HMP DCC efforts is evidenced by a consistently high rate of user access to the web resources, with 9,000-12,000 user sessions each month.
“The success of these efforts is evidenced by a consistently high rate of user access to the web resources, with 9,000–12,000 user sessions each month”
Quote in the paperNot a substantial claim- Rests on an assumption: A consistently high rate of user access to the DCC web resources is a valid indicator of the success of the HMP DCC data-sharing efforts.
- Findingreasoning
The IBDMDB defined a dysbiosis score that called out highly divergent microbial compositions, which share many features common to an overall inflammatory response, such as tolerance to oxidative stress.
“the study defined a dysbiosis score that called out highly divergent microbial compositions, which share many features common to an overall inflammatory response (for example, tolerance to oxidative stress).”
Quote in the paperSubstantial claim - Findingcitation
The HMP2 studies together provide information and insights about microbial dynamics, associated human host responses, and microbial inter-relationships.
“These studies, which have now reached the first stage of completion49–51, together provide a wealth of information and insights about not only microbial dynamics, but also associated human host responses and microbial inter-relationships.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [49] Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature https://doi.org/10.1038/s41586-019-1237-9 (2019). HMP2 Inflammatory Bowel Disease Multi’omics Database (IBDMDB) study profiling 2,965 stool, biopsy, and blood specimens from 132 individuals with Crohn’s disease, ulcerative colitis or neither (control subjects) followed longitudinally for one year each; multi-omic profiling identified taxa, expressed functions, metabolite pools, and host gene expression that were disrupted in the gut during increased disease activity, as well as systemic eff source
Not checked in the free run.
- cites [50] Fettweis, J. M. et al. The vaginal microbiome and preterm birth. Nat. Med. https://doi.org/10.1038/s41591-019-0450-2 (2019). HMP2 preterm birth study of 1,527 pregnancies focusing on 45 spontaneous preterm birth events and 90 case-matched controls, together yielding over 12,000 multi-omically profiled samples; 16S rRNA gene, metagenomic, metatranscriptomic and cytokine profiles identified differential levels of Lactobacillus crispatus, BVAB1, Sneathia amnii, TM7-H1, and additional taxa associated with preterm deliveries. source
Not checked in the free run.
- cites [51] Zhou, W. et al. Longitudinal multi-omics of host–microbe dynamics in prediabetes. Nature https://doi.org/10.1038/s41586-019-1236-x (2019). HMP2 prediabetes study of 106 participants spanning 1,092 visits, profiling host transcriptomes, metabolomes, cytokines, and proteomes, as well as microbial taxonomic and genomic changes during healthy periods, respiratory viral infections, immunizations, and other perturbations; most molecular profiles were personalized, perturbed during infections and immunizations, and associated with changes in insulin sensitivity. source
Not checked in the free run.
- Findingown data
Microbial compositions in patients with IBD often revert to more control-like, baseline configurations when the disease is not active.
“showing that microbial compositions in patients with IBD often revert to more control-like, ‘baseline’ configurations when the disease is not active.”
Quote in the paperSubstantial claim - Findingown data
The IBDMDB constructed a network of more than 2,900 significant host and microbial cellular and molecular interactors during IBD.
“the study’s long-term, complementary molecular measurements enabled the construction of a network of more than 2,900 significant host and microbial cellular and molecular interactors during IBD”
Quote in the paperSubstantial claim - Findingcitation
Taking millions of measurements per individual over time enabled the early detection of potential disease states.
“We found that taking millions of measurements per individual over time enabled the early detection of potential disease states51,87.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [51] Zhou, W. et al. Longitudinal multi-omics of host–microbe dynamics in prediabetes. Nature https://doi.org/10.1038/s41586-019-1236-x (2019). HMP2 prediabetes study of 106 participants spanning 1,092 visits, profiling host transcriptomes, metabolomes, cytokines, and proteomes, as well as microbial taxonomic and genomic changes during healthy periods, respiratory viral infections, immunizations, and other perturbations; most molecular profiles were personalized, perturbed during infections and immunizations, and associated with changes in insulin sensitivity. source
Not checked in the free run.
- cites [87] Schüssler-Fiorenza Rose, S. et al. A longitudinal big data approach for precision health. Nat. Med. 25, 792–804 (2019). HMP2 Integrative Personalized ’Omics Profiling of 109 individuals enriched for risk of type 2 diabetes studied genomically, immunologically, transcriptomically, proteomically, metabolomically, and using wearable monitoring; multiple molecular pathways were associated with metabolic, cardiovascular and oncological pathophysiology, and a subset were predictive of insulin resistance. source
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- Findingown data
Community state and immune responses are distinctly less stable in participants with IBD than in control, healthy individuals.
“highlighting ways in which community state and immune responses are distinctly less stable in participants with IBD than in control, healthy individuals.”
Quote in the paperSubstantial claim - Findingreasoning
An analyte test was devised as part of the study to differentiate ultimately insulin-resistant from insulin-sensitive participants.
“and an analyte test was devised as part of the study in order to differentiate them.”
Quote in the paperSubstantial claim - Findingreasoning
IBDMDB identified a variety of differences in the microbiome and host immune response over time during the course of IBD.
“This unique study design allowed the IBDMDB to identify a variety of differences in the microbiome and host immune response over time during the course of the disease.”
Quote in the paperSubstantial claim - Findingown data
Participants who were ultimately insulin-resistant had distinguishable molecular and microbial patterns at baseline from those who were ultimately insulin-sensitive.
“Participants who were ultimately insulin-resistant had distinguishable molecular and microbial patterns at baseline from those who were ultimately insulin-sensitive”
Quote in the paperSubstantial claim - Interpretationreasoning
MOMS-PI identified associations between the vaginal microbiota, host response, and pregnancy outcomes that are consistent with the involvement of microorganisms ascending from the vagina in at least some cases of spontaneous PTB.
“The MOMS-PI research group identified intriguing associations between the vaginal microbiota, host response, and pregnancy outcomes that are consistent with the involvement of microorganisms ascending from the vagina in at least some cases of spontaneous PTB.”
Quote in the paperSubstantial claim- Rests on an assumption: Environmental factors, including the microbiome of the female reproductive tract, are important contributors to prematurity.
- Interpretationreasoning
The vaginal microbiome might mediate a link between PTB risk and vitamin D deficiency.
“suggesting that the vaginal microbiome might mediate a link between PTB risk and vitamin D deficiency64.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [64] Zhou, S. S., Tao, Y. H., Huang, K., Zhu, B. B. & Tao, F. B. Vitamin D and risk of preterm birth: Up-to-date meta-analysis of randomized controlled trials and observational studies. J. Obstet. Gynaecol. Res. 43, 247–256 (2017). source
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- Interpretationown data
These results illustrate the heterogeneity of T2D development.
“These results, together with detailed characterization of glucose dysregulation over time, illustrate the heterogeneity of T2D development.”
Quote in the paperSubstantial claim - Interpretationown data
Using big data, including the microbiome, can better manage human health.
“demonstrating the power of using big data, including the microbiome, to better manage human health.”
Quote in the paperSubstantial claim - Premisecitation
No comprehensive model of specific microbial, molecular, and immune interactions yet exists to explain IBD onset and dynamic progression.
“no comprehensive model of specific microbial, molecular, and immune interactions yet exists to explain the disease’s onset and dynamic progression.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [71] Morgan, X. C. et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 13, R79 (2012). source
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- cites [72] Hall, A. B. et al. A novel Ruminococcus gnavus clade enriched in inflammatory bowel disease patients. Genome Med. 9, 103 (2017).
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- Premisecitation
T2D is characterized by complex host-microbiome interactions.
“T2D is characterized by complex host–microbiome interactions81,82”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [81] Pickup, J. C. Inflammation and activated innate immunity in the pathogenesis of type 2 diabetes. Diabetes Care 27, 813–823 (2004). source
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- cites [82] Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012). source
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- Interpretationreasoning
Strong local perturbations initiated from either the host or microbial side can induce subsequent spatiotemporal responses that can continue over time and/or in other tissues.
“Strong local perturbations initiated from either the host or microbial side can induce subsequent spatiotemporal responses that can continue over time and/or in other tissues”
Quote in the paperSubstantial claim - Interpretationreasoning
At microbiome-relevant time scales, repeated measures as rapid as days to weeks were necessary to capture the most specific host-microbiome interactions.
“This is particularly true at microbiome-relevant time scales, for which repeated measures as rapid as days to weeks were necessary to capture the most specific host–microbiome interactions.”
Quote in the paperSubstantial claim - Interpretationcitation
The differing co-association patterns indicate different patterns of host-microbiome interactions in insulin-resistant and insulin-sensitive groups.
“indicating different patterns of host–microbiome interactions in the two groups51.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [51] Zhou, W. et al. Longitudinal multi-omics of host–microbe dynamics in prediabetes. Nature https://doi.org/10.1038/s41586-019-1236-x (2019). HMP2 prediabetes study of 106 participants spanning 1,092 visits, profiling host transcriptomes, metabolomes, cytokines, and proteomes, as well as microbial taxonomic and genomic changes during healthy periods, respiratory viral infections, immunizations, and other perturbations; most molecular profiles were personalized, perturbed during infections and immunizations, and associated with changes in insulin sensitivity. source
Not checked in the free run.
- Interpretationreasoning
Networks of mechanistic associations such as the IBD network may provide the key to disentangling the complex system of interactions that results in chronic inflammation in IBD and in other systemic microbiome-linked immune diseases.
“Networks of mechanistic associations such as this may provide the key to disentangling the complex system of interactions that results in chronic inflammation in IBD and in other systemic microbiome-linked immune diseases.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Host-microbiome interactions have both localized and systemic effects.
“It is thus clear from these and other studies that host–microbiome interactions have both localized and systemic effects.”
Quote in the paperSubstantial claim
- May combine two claims. 15
- Classified as probably not a substantial claim. 10
May combine two claims.
FindingcitationHMP1 found that the taxonomic composition of the microbiome alone was often not a good correlate with host phenotype; host phenotype tended to be better predicted by prevalent microbial molecular function or personalized strain-specific makeup.
“One of the main findings of the HMP1 was that the taxonomic composition of the microbiome alone was often not a good correlate with host phenotype—this tended to be better predicted by prevalent microbial molecular function or personalized strain-specific makeup21.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [21] Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012). HMP1 study analysing 5,177 16S rRNA gene sequencng profiles and 681 shotgun metagenomes spanning up to 18 body sites and three time points each from 242 healthy adults; established baseline ranges of taxonomic diversity within and between body sites, as well as strain personalization and functional commonalities. source
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Probably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenTogether, the HMP1 and HMP2 phases have produced a total of 42 terabytes of multi-omic data.
“Together, the HMP1 and HMP2 phases have produced a total of 42 terabytes of multi-omic data”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataDuring RVI, insulin-resistant participants showed substantially decreased and delayed inflammatory responses, including the acute phase response and IL-1 signalling, and altered gut microbial changes compared with insulin-sensitive participants.
“For example, during RVI, insulin-resistant participants showed substantially decreased and delayed inflammatory responses (for example, the acute phase response and IL-1 signalling) and altered gut microbial changes when compared with insulin-sensitive participants (for example, in Lachnospiraceae and Rikenellaceae but not bacilli).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe DCC website allows users to find, query, search, visualize, and download data from thousands of samples with associated metadata.
“The DCC website allows users to find, query, search, visualize, and download data from thousands of samples with associated metadata.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationHMP1 studies established typical ranges, for some populations, of microbial membership and enzymatic repertoires across the body, combinations of metabolic functions that were either prevalent or strain-specific, and some host factors such as race or ethnicity that determine this variation.
“Studies of both a baseline adult population21–23 and ‘demonstration’ populations with specific disease states established typical ranges (for some populations) of microbial membership and enzymatic repertoires across the body, combinations of metabolic functions that were either prevalent or strain-specific, and some of the host factors (such as race or ethnicity) that determine this variation.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [21] Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012). HMP1 study analysing 5,177 16S rRNA gene sequencng profiles and 681 shotgun metagenomes spanning up to 18 body sites and three time points each from 242 healthy adults; established baseline ranges of taxonomic diversity within and between body sites, as well as strain personalization and functional commonalities. source
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- cites [22] Human Microbiome Project Consortium. A framework for human microbiome research. Nature 486, 215–221 (2012). source
Not checked in the free run.
- cites [23] Lloyd-Price, J. et al. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature 550, 61–66 (2017). Second wave of HMP1 data (HMP1-II) extending the analysis and data resource to 2,355 total shotgun metagenomes from 265 healthy adults; quantified strain personalization and retention dynamics over time, as well as identifying niche-specific and host-associated microbial community functions. source
Not checked in the free run.
May combine two claims.
Findingown dataThe data led to microbially linked, clinically actionable health discoveries in a number of diseases in addition to T2D, including metabolic disease, cardiovascular disease, haematological or oncological conditions, and other areas; these signs were often present before symptom onset.
“Overall, the data led to microbially linked, clinically actionable health discoveries in a number of diseases in addition to T2D, including metabolic disease, cardiovascular disease, haematological or oncological conditions, and other areas; these signs were often present before symptom onset”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataEarly detection of T2D developed differently among participants and was better detectable with varied assays; some individuals first exhibited measurements in the diabetic range on tests of fasting glucose, whereas others did so on tests of haemoglobin A1c, oral glucose tolerance tests, or continuous glucose monitoring.
“These included early detection of T2D, which developed differently among participants and was better detectable with varied assays; for example, some individuals first exhibited measurements in the diabetic range on tests of fasting glucose, whereas others did so on tests of haemoglobin A1c, oral glucose tolerance tests, or even continuous glucose monitoring.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe dysbiosis was not unique to the microbial response to inflammation and was associated with other host and biochemical alterations.
“This dysbiosis was not unique to the microbial response to inflammation, however, and was associated with other host and biochemical alterations”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe main microbial contributors to large-scale shifts from one time point to the next largely mirrored the differences observed in dysbiosis, and the shifts frequently marked entrance into or exit from periods of dysbiosis.
“The main microbial contributors to these large-scale shifts from one time point to the next largely mirrored the differences observed in dysbiosis, and the shifts frequently marked the entrance into or exit from periods of dysbiosis.”
Quote in the paperSubstantial claimMay combine two claims.
FindingreasoningThere were fewer changes in nasal microbiota in insulin-resistant participants, and both the richness and diversity of nasal microorganisms decreased during RVI in insulin-sensitive but not insulin-resistant participants.
“Accordingly, there were fewer changes in nasal microbiota in insulin-resistant participants, and both the richness and the diversity of nasal microorganisms decreased during RVI in insulin-sensitive but not insulin-resistant participants.”
Quote in the paperSubstantial claimMay combine two claims.
FindingcitationHMP1 yielded community resources including nucleotide sequences of microorganisms and communities, protocols for reproducible body-wide microbiome sampling and data generation, and computational methods for microbiome analysis and epidemiology.
“This first phase of the HMP (HMP1) thus yielded a wealth of community resources: nucleotide sequences of microorganisms and communities from a large number of isolates, individuals, and populations (http://hmpdacc.org)34–37; protocols to support reproducible body-wide microbiome sampling and data generation38–40; and computational methods for microbiome analysis and epidemiology41–47.”
Quote in the paperNot a substantial claim0 of 10 sources checked- cites [34] Fodor, A. A. et al. The “most wanted” taxa from the human microbiome for whole genome sequencing. PLoS One 7, e41294 (2012).
Not checked in the free run.
- cites [35] Nelson, K. E. et al. A catalog of reference genomes from the human microbiome. Science 328, 994–999 (2010). source
Not checked in the free run.
- cites [36] Wylie, K. M. et al. Novel bacterial taxa in the human microbiome. PLoS One 7, e35294 (2012).
Not checked in the free run.
- cites [37] Li, K., Bihan, M., Yooseph, S. & Methé, B. A. Analyses of the microbial diversity across the human microbiome. PLoS One 7, e32118 (2012).
Not checked in the free run.
- cites [38] Aagaard, K. et al. The Human Microbiome Project strategy for comprehensive sampling of the human microbiome and why it matters. FASEB J. 27, 1012–1022 (2013). source
Not checked in the free run.
- cites [39] Schloss, P. D., Gevers, D. & Westcott, S. L. Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS One 6, e27310 (2011).
Not checked in the free run.
- cites [40] Jumpstart Consortium Human Microbiome Project Data Generation Working Group. Evaluation of 16S rDNA-based community profiling for human microbiome research. PLoS One 7, e39315 (2012).
Not checked in the free run.
- cites [41] Gevers, D., Pop, M., Schloss, P. D. & Huttenhower, C. Bioinformatics for the Human Microbiome Project. PLoS Comput. Biol. 8, e1002779 (2012). source
Not checked in the free run.
- cites [42] Huse, S. M., Ye, Y., Zhou, Y. & Fodor, A. A. A core human microbiome as viewed through 16S rRNA sequence clusters. PLoS One 7, e34242 (2012).
Not checked in the free run.
- cites [43] Markowitz, V. M. et al. IMG/M-HMP: a metagenome comparative analysis system for the Human Microbiome Project. PLoS One 7, e40151 (2012).
Not checked in the free run.
May combine two claims.
Findingown dataIn numerous cases, the microbiome of a participant with IBD changed completely over the course of only weeks, whereas such shifts were rare in individuals without IBD.
“In numerous cases, the microbiome of a participant with IBD changed completely over the course of only weeks (measured as maximal Bray–Curtis dissimilarity to earlier samples from the same subject), whereas such shifts were rare in individuals without IBD.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenAll data on the DCC is available for unrestricted use, with a subset of project metadata shared when permitted by IRBs and other restricted data available through controlled access at dbGaP.
“All data on the DCC is available for unrestricted use, with a subset of project metadata also being shared when permitted by institutional review boards (IRBs), and other restricted data (for example, human genome sequences and protected metadata) available through controlled access at dbGaP”
Quote in the paperNot a substantial claimMay combine two claims.
FindingreasoningThe host and biochemical alterations associated with dysbiosis included large shifts in acylcarnitine pools and bile acids, increased serum antibody levels, and alterations in transcription for several microbial species.
“These included large shifts in acylcarnitine pools and bile acids, increased serum antibody levels, and alterations in transcription for several microbial species.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
FindingreasoningThe HMP2 provides a rich multi-omic data resource to be mined by future work.
“and together they provide a rich multi-omic data resource to be mined by future work (http://www.ihmpdcc.org).”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone givenA few mechanisms were identified by which signals in the gut can be transmitted to systemic conditions such as diabetes, but not the specific small molecules or immune cell subsets by which they are likely to be transmitted.
“A few mechanisms were identified by which signals in the gut can be transmitted to systemic conditions such as diabetes, but not the specific small molecules or immune cell subsets by which they are likely to be transmitted”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe formal data models and associated entity relationship schemas produced by all phases of the HMP are freely available at https://github.com/ihmpdcc/osdf-schemas.
“The formal data models and associated entity relationship schemas produced by all phases of the HMP are freely available at https://github.com/ihmpdcc/osdf-schemas.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe microbiome has long been implicated in IBD, potentially as a causative or risk factor.
“The microbiome has long been implicated in IBD, potentially as a causative or risk factor66,67”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [66] Manichanh, C. et al. Reduced diversity of faecal microbiota in Crohn's disease revealed by a metagenomic approach. Gut 55, 205–211 (2005). source
Not checked in the free run.
- cites [67] Frank, D. N. et al. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc. Natl Acad. Sci. USA 104, 13780–13785 (2007). source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
PremisecitationPTB has a complex aetiology.
“It is clear that PTB has a complex aetiology56.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [56] Romero, R., Dey, S. K. & Fisher, S. J. Preterm labor: one syndrome, many causes. Science 345, 760–765 (2014). source
Not checked in the free run.
May combine two claims.
Interpretationown dataThe combination of shotgun metagenomics, untargeted metabolomics, and immunoprofiling was particularly effective, as in all projects this subset of molecular measurements tended to efficiently capture interpretable host and microbial properties linked to disease.
“The combination of shotgun metagenomics, untargeted metabolomics, and immunoprofiling was particularly effective, as in all projects this subset of molecular measurements tended to efficiently capture interpretable host and microbial properties that are linked to disease.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe microbiome is an integral component of human biology, with a major role in health and well-being.
“The collective results of the NIH HMP projects, alongside many other studies, show that the microbiome is an integral component of human biology, with a major role in health and well-being.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenHuman-associated microbiology now clearly extends beyond infectious and gastrointestinal diseases to areas including metabolism, neoplasia, maternal and child health, and central nervous system function.
“Human-associated microbiology now clearly extends beyond infectious and gastrointestinal diseases to areas barely imaginable a few decades ago, including metabolism, neoplasia, maternal and child health, and central nervous system function.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationThe HMP DCC efforts are by design consistent with the NIH's stated goals to make all data generated from NIH funding findable, accessible, interoperable, and reusable.
“The HMP DCC efforts are thus by design consistent with the NIH’s stated goals to make all data generated from NIH funding findable, accessible, interoperable, and reusable88.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [88] Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016). source
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Study design and conductnone givenEach HMP2 study was necessarily carried out within a geographically and genetically constrained population.
“each HMP2 study was necessarily carried out within a geographically and genetically constrained population”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Little is known about systemic alterations during prediabetes, their effects on biological processes, or the critical transition to full-blown T2D.
“but little is known about systemic alterations during prediabetes, their effects on biological processes, or the critical transition to full-blown T2D.”
The paper relies on this without support.
Prediabetes and T2D are often associated with insulin resistance.
“Prediabetes and T2D are often associated with insulin resistance”
The paper relies on this without support.
Environmental factors, including the microbiome of the female reproductive tract, are important contributors to prematurity.
“Environmental factors, including the microbiome of the female reproductive tract, are important contributors to prematurity.”
The paper relies on this without support.
The three HMP2 studies' multi-omic measurements and populations are sufficiently comparable to allow identification of common host–microbiome features across them.
“a surprising range of host–microbiome immune and ecological features were common among them”
The paper relies on this without support.
A consistently high rate of user access to the DCC web resources is a valid indicator of the success of the HMP DCC data-sharing efforts.
“The success of these efforts is evidenced by a consistently high rate of user access to the web resources, with 9,000–12,000 user sessions each month”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Vaginal probiotics, administered topically or orally, are promising to restore balance in the vaginal microbial community and prevent recurrent infections by inhibiting pathogen growth.
“Vaginal probiotics, administered either topically or orally, are currently promising to restore the balance in the vaginal microbial community and preventing recurrent infections by inhibiting pathogen growth [12].”
From Women's health is a team effort: probiogenomics supports the development of a multi-species vaginal probiotic (2026) · cites this paper as [12]
The section “The vaginal microbiome, pregnancy and preterm birth” describes changes in vaginal microbial communities and associations with preterm birth, not probiotic treatment. Searches for vaginal probiotics, oral or topical administration, and recurrent infections found no passage addressing the claimed intervention or its proposed inhibition of pathogen growth. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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The vaginal microbiome and preterm birth
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Self-citationThe human gut microbiome in early-onset type 1 diabetes from the TEDDY study
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- Authors
- The Integrative HMP (iHMP) Research Network Consortium, Proctor, L. M., Creasy, H. H., Fettweis, J. M., Lloyd-Price, J., Mahurkar, A., Zhou, W., Buck, G. A., Snyder, M. P., Strauss, J. F., Weinstock, G. M., White, O., & Huttenhower, C.
- Affiliations
- National Institutes of Health (US)
- University of Maryland, Baltimore (US)
- Virginia Commonwealth University (US)
- Stanford Medicine (US)
- The Jackson Laboratory for Genomic Medicine (US)
- Broad Institute (US)
- Corresponding author
- Buck, Gregory A.
- Snyder, Michael P.
- Strauss, Jerome F.
- Weinstock, George M.
- White, Owen
- Huttenhower, Curtis
- ORCID
- Venue
- Nature
- Published
- 2019-05-29
- Volume
- 569
- Issue
- 7758
- Pages
- 641-648
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6784865
- OpenAlex
- W2990249310
- Semantic Scholar
- 67e3a32761e08ffca7868bca6d1388e6a297ce43
- Cited by
- 1,381
- References
- 89
- Citations by year
- 2026: 193 · 2025: 184 · 2024: 196 · 2023: 198 · 2022: 203 · 2021: 195 · 2020: 166 · 2019: 44 · 2016: 1 · 2012: 1
- FWCI
- 50.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Office of Dietary Supplements
- National Institutes of Health
- National Institute of Allergy and Infectious Diseases (UH3AI083263)
- National Institute of Diabetes and Digestive and Kidney Diseases (U54DK102556, U54DK102557)
- National Institute of Dental and Craniofacial Research (U54DE023786)
- Office of Research on Women's Health
- National Center for Complementary and Integrative Health
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (U54HD080784, R01HD092415)
- Common Fund
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- human microbiome, host-microbiome interactions, pregnancy, preterm birth, inflammatory bowel disease, prediabetes, stress, microbiome dynamics, multi-omics
- MeSH terms
- Gastrointestinal Microbiome, Host Microbial Interactions, Diet, Female, Humans, Infant, Newborn, Infant, Premature, Infections, National Institutes of Health (U.S.), Prediabetic State, Pregnancy, Research, Time Factors, United States, Vagina, Inflammatory Bowel Diseases, Microbiota
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