Longitudinal multi-omics of host–microbe dynamics in prediabetes
Open accessAbstract
Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host–microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states.
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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.”
From The Integrative Human Microbiome Project (2019) · cites this paper as [51]
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.”
From The Integrative Human Microbiome Project (2019) · cites this paper as [51]
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.”
From The Integrative Human Microbiome Project (2019) · cites this paper as [51]
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.”
From The Integrative Human Microbiome Project (2019) · cites this paper as [51]
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.”
From The Integrative Human Microbiome Project (2019) · cites this paper as [51]
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Valles-Colomer, M., Menni, C., Berry, S. E., Valdes, A. M., Spector, T. D., & Segata, N. (2023) · Nature Medicine · doi:10.1038/s41591-023-02260-4 · Cited by 114 · see citing works
Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models
Allesøe, R. L., Lundgaard, A. T., Hernández Medina, R., Aguayo-Orozco, A., Johansen, J., Nissen, J. N., Brorsson, C., Mazzoni, G., Niu, L., Biel, J. H., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01520-x · Cited by 111 · see citing works
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- Authors
- Zhou, W., Sailani, M. R., Contrepois, K., Zhou, Y., Ahadi, S., Leopold, S. R., Zhang, M. J., Rao, V., Avina, M., Mishra, T., Johnson, J., Lee-McMullen, B., Chen, S., Metwally, A. A., Tran, T. D. B., Nguyen, H., Zhou, X., Albright, B., Hong, B.-Y., Petersen, L., Bautista, E., Hanson, B., Chen, L., Spakowicz, D., Bahmani, A., Salins, D., Leopold, B., Ashland, M., Dagan-Rosenfeld, O., Rego, S., Limcaoco, P., Colbert, E., Allister, C., Perelman, D., Craig, C., Wei, E., Chaib, H., Hornburg, D., Dunn, J., Liang, L., Rose, S. M. S.-F., Kukurba, K., Piening, B., Rost, H., Tse, D., McLaughlin, T., Sodergren, E., Weinstock, G. M., & Snyder, M.
- Affiliations
- Stanford Medicine (US)
- The Jackson Laboratory for Genomic Medicine (US)
- Stanford University (US)
- VA Palo Alto Health Care System (US)
- Providence Portland Medical Center (US)
- Corresponding author
- Weinstock, George M.
- Snyder, Michael
- ORCID
- Zhou, Wenyu
- Sailani, M. Reza
- Contrepois, Kévin
- Zhou, Yanjiao
- Ahadi, Sara
- Leopold, Shana R.
- Zhang, Martin J.
- Rao, Varsha
- Mishra, Tejaswini
- Johnson, Jethro
- Chen, Songjie
- Metwally, Ahmed A.
- Nguyen, Hoan
- Zhou, Xin
- Hong, Bo-Young
- Petersen, Lauren
- Bautista, Eddy
- Hanson, Blake
- Chen, Lei
- Spakowicz, Daniel
- Bahmani, Amir
- Salins, Denis
- Leopold, Benjamin
- Ashland, Melanie
- Rego, Shannon
- Perelman, Dalia
- Craig, Colleen
- Hornburg, Daniel
- Dunn, Jessilyn
- Liang, Liang
- Rose, Sophia Miryam Schüssler-Fiorenza
- Piening, Brian
- Tse, David
- McLaughlin, Tracey
- Sodergren, Erica
- Weinstock, George M.
- Snyder, Michael
- Venue
- Nature
- Published
- 2019-05-29
- Volume
- 569
- Issue
- 7758
- Pages
- 663-671
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6666404
- OpenAlex
- W2948018512
- Cited by
- 583
- References
- 72
- Citations by year
- 2026: 63 · 2025: 59 · 2024: 69 · 2023: 77 · 2022: 107 · 2021: 95 · 2020: 90 · 2019: 20
- FWCI
- 59.56
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (161005)
- National Institutes of Health (UL1TR001085, UL1TR003142, 1s10od020141-01, TR001085)
- National Heart, Lung, and Blood Institute (K12HL120001)
- National Human Genome Research Institute (R01HG008164)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK110186, U54DK102556, P30DK116074)
- National Institute of Environmental Health Sciences (K08ES028825)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Keywords
- prediabetes, type 2 diabetes, host-microbe interactions, gut microbiota, insulin resistance, respiratory viral infections, immunization, transcriptome, metabolome, proteome, cytokines, inflammation, C-reactive protein, multi-omics, longitudinal profiling
- MeSH terms
- Gastrointestinal Microbiome, Host Microbial Interactions, Adult, Aged, Anti-Bacterial Agents, Diabetes Mellitus, Type 2, Female, Glucose, Humans, Inflammation, Influenza Vaccines, Insulin, Insulin Resistance, Longitudinal Studies, Male, Middle Aged, Prediabetic State, Respiratory Tract Infections, Stress, Physiological, Vaccination, Cohort Studies, Biomarkers, Computational Biology, Proteome, Transcriptome, Microbiota, Healthy Volunteers, Datasets as Topic
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