The geometry of clinical labs and wellness states from deeply phenotyped humans.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
11 06 2021
11 06 2021
Historique:
received:
17
08
2020
accepted:
17
05
2021
entrez:
12
6
2021
pubmed:
13
6
2021
medline:
1
7
2021
Statut:
epublish
Résumé
Longitudinal multi-omics measurements are highly valuable in studying heterogeneity in health and disease phenotypes. For thousands of people, we have collected longitudinal multi-omics data. To analyze, interpret and visualize this extremely high-dimensional data, we use the Pareto Task Inference (ParTI) method. We find that the clinical labs data fall within a tetrahedron. We then use all other data types to characterize the four archetypes. We find that the tetrahedron comprises three wellness states, defining a wellness triangular plane, and one aberrant health state that captures aspects of commonality in movement away from wellness. We reveal the tradeoffs that shape the data and their hierarchy, and use longitudinal data to observe individual trajectories. We then demonstrate how the movement on the tetrahedron can be used for detecting unexpected trajectories, which might indicate transitions from health to disease and reveal abnormal conditions, even when all individual blood measurements are in the norm.
Identifiants
pubmed: 34117230
doi: 10.1038/s41467-021-23849-8
pii: 10.1038/s41467-021-23849-8
pmc: PMC8196202
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3578Références
National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. (National Academies Press (US), 2011).
Hood, L. & Flores, M. A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. N. Biotechnol. 29, 613–624 (2012).
pubmed: 22450380
doi: 10.1016/j.nbt.2012.03.004
Price, N. D. et al. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat. Biotechnol. 35, 747–756 (2017).
pubmed: 28714965
pmcid: 5568837
doi: 10.1038/nbt.3870
Collins, F. S. & Varmus, H. A new initiative on precision. Med. N. Engl. J. Med. 372, 793–795 (2015).
pubmed: 25635347
doi: 10.1056/NEJMp1500523
Karczewski, K. J. & Snyder, M. P. Integrative omics for health and disease. Nat. Rev. Genet. 19, 299–310 (2018).
pubmed: 29479082
pmcid: 5990367
doi: 10.1038/nrg.2018.4
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
pubmed: 30305743
pmcid: 6786975
doi: 10.1038/s41586-018-0579-z
Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Genome Biol. 18, 83 (2017).
pubmed: 28476144
pmcid: 5418815
doi: 10.1186/s13059-017-1215-1
Ghaemi, M. S. et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics 35, 95–103 (2019).
pubmed: 30561547
doi: 10.1093/bioinformatics/bty537
Shomorony, I. et al. An unsupervised learning approach to identify novel signatures of health and disease from multimodal data. Genome Med. 12, 7 (2020).
pubmed: 31924279
pmcid: 6953286
doi: 10.1186/s13073-019-0705-z
Dutton, G. Arivale brings genomics to the people. Genet. Eng. Biotechnol. N. 35, 10–11 (2015).
Zubair, N. et al. Genetic predisposition impacts clinical changes in a lifestyle coaching program. Sci. Rep. 9, 1–11 (2019).
doi: 10.1038/s41598-019-43058-0
Earls, J. C. et al. Multi-omic biological age estimation and its correlation with wellness and disease phenotypes: a longitudinal study of 3,558 individuals. J. Gerontol. A. Biol. Sci. Med. Sci. 74, S52–S60 (2019).
pubmed: 31724055
pmcid: 6853785
doi: 10.1093/gerona/glz220
Wilmanski, T. et al. Blood metabolome predicts gut microbiome α-diversity in humans. Nat. Biotechnol. 37, 1217–1228 (2019).
pubmed: 31477923
doi: 10.1038/s41587-019-0233-9
Pinu, F. R. et al. Systems biology and multi-omics integration: viewpoints from the metabolomics research community. Metabolites 9, 76 (2019).
pmcid: 6523452
doi: 10.3390/metabo9040076
Huang, S., Chaudhary, K. & Garmire, L. X. More is better: recent progress in multi-omics data integration methods. Front. Genet. 8, 1–4 (2017).
Bersanelli, M. et al. Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics 17, S15 (2016).
doi: 10.1186/s12859-015-0857-9
Wilmanski, T. et al. Gut microbiome pattern reflects healthy aging and predicts extended survival in humans (2020) https://doi.org/10.1101/2020.02.26.966747 .
Maaten, Lvander & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Tini, G., Marchetti, L., Priami, C. & Scott-Boyer, M.-P. Multi-omics integration—a comparison of unsupervised clustering methodologies. Brief. Bioinform 20, 1269–1279 (2019).
pubmed: 29272335
doi: 10.1093/bib/bbx167
Peng, C. et al. A latent unknown clustering integrating multi-omics data (LUCID) with phenotypic traits. Bioinformatics 36, 842–850 (2020).
pubmed: 31504184
doi: 10.1093/bioinformatics/btz667
Su, M.-W. et al. Blood multiomics reveal insights into population clusters with low prevalence of diabetes, dyslipidemia and hypertension. PLOS ONE 15, e0229922 (2020).
pubmed: 32134946
pmcid: 7058291
doi: 10.1371/journal.pone.0229922
Chauvel, C., Novoloaca, A., Veyre, P., Reynier, F. & Becker, J. Evaluation of integrative clustering methods for the analysis of multi-omics data. Brief. Bioinform. https://doi.org/10.1093/bib/bbz015 .
Argelaguet, R. et al. Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).
pubmed: 29925568
pmcid: 6010767
doi: 10.15252/msb.20178124
Argelaguet, R. et al. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 21, 111 (2020).
pubmed: 32393329
pmcid: 7212577
doi: 10.1186/s13059-020-02015-1
Hart, Y. et al. Inferring biological tasks using Pareto analysis of high-dimensional data. Nat. Methods 12, 233–235 (2015).
pubmed: 25622107
doi: 10.1038/nmeth.3254
Shoval, O. et al. Evolutionary trade-offs, Pareto optimality, and the geometry of phenotype space. Science 336, 1157–1160 (2012).
pubmed: 22539553
doi: 10.1126/science.1217405
Sheftel, H., Shoval, O., Mayo, A. & Alon, U. The geometry of the Pareto front in biological phenotype space. Ecol. Evol. 3, 1471–1483 (2013).
pubmed: 23789060
pmcid: 3686184
doi: 10.1002/ece3.528
Hausser, J. et al. Tumor diversity and the trade-off between universal cancer tasks. Nat. Commun. 10, 1–13 (2019).
doi: 10.1038/s41467-019-13195-1
Korem, Y. et al. Geometry of the gene expression space of individual cells. PLoS Comput. Biol. 11, e1004224 (2015).
pubmed: 26161936
pmcid: 4498931
doi: 10.1371/journal.pcbi.1004224
Single Cell Transcriptional Archetypes of Airway Inflammation in Cystic Fibrosis | medRxiv. https://www.medrxiv.org/content/10.1101/2020.03.06.20032292v1 .
Duvallet, C., Gibbons, S. M., Gurry, T., Irizarry, R. A. & Alm, E. J. Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. Nat. Commun. 8, 1–10 (2017).
doi: 10.1038/s41467-017-01973-8
Pascal, V. et al. A microbial signature for Crohn’s disease. Gut 66, 813–822 (2017).
pubmed: 28179361
doi: 10.1136/gutjnl-2016-313235
Chang, J. Y. et al. Decreased diversity of the fecal Microbiome in recurrent Clostridium difficile-associated diarrhea. J. Infect. Dis. 197, 435–438 (2008).
pubmed: 18199029
doi: 10.1086/525047
Mosca, A., Leclerc, M. & Hugot, J. P. Gut microbiota diversity and human diseases: should we reintroduce key predators in our ecosystem? Front. Microbiol. 7, 455 (2016).
pubmed: 27065999
pmcid: 4815357
doi: 10.3389/fmicb.2016.00455
Mancabelli, L. et al. Identification of universal gut microbial biomarkers of common human intestinal diseases by meta-analysis. FEMS Microbiol. Ecol. 93, 1–10 (2017).
Miquel, S. et al. Faecalibacterium prausnitzii and human intestinal health. Curr. Opin. Microbiol. 16, 255–261 (2013).
pubmed: 23831042
doi: 10.1016/j.mib.2013.06.003
Wexler, A. G. & Goodman, A. L. An insider’s perspective: bacteroides as a window into the microbiome. Nat. Microbiol. 2, 1–11 (2017).
doi: 10.1038/nmicrobiol.2017.26
Gauffin Cano, P., Santacruz, A., Moya, Á. & Sanz, Y. Bacteroides uniformis CECT 7771 ameliorates metabolic and immunological dysfunction in mice with high-fat-diet induced obesity. PLoS ONE 7, (2012).
Atarashi, K. et al. Induction of colonic regulatory T cells by indigenous Clostridium species. Science 331, 337–341 (2011).
pubmed: 21205640
doi: 10.1126/science.1198469
Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 842–853 (2016).
pubmed: 27133167
pmcid: 4950857
doi: 10.1016/j.cell.2016.04.007
Rosenshine, I. et al. A pathogenic bacterium triggers epithelial signals to form a functional bacterial receptor that mediates actin pseudopod formation. EMBO J. 15, 2613–2624 (1996).
pubmed: 8654358
pmcid: 450196
doi: 10.1002/j.1460-2075.1996.tb00621.x
Kaper, J. B., Nataro, J. P. & Mobley, H. L. T. Pathogenic Escherichia coli. Nat. Rev. Microbiol. 2, 123–140 (2004).
pubmed: 15040260
doi: 10.1038/nrmicro818
Raymond, F. et al. The initial state of the human gut microbiome determines its reshaping by antibiotics. ISME J. 10, 707–720 (2016).
pubmed: 26359913
doi: 10.1038/ismej.2015.148
Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009).
pubmed: 19043404
doi: 10.1038/nature07540
Holmes, I., Harris, K. & Quince, C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLOS ONE 7, e30126 (2012).
pubmed: 22319561
pmcid: 3272020
doi: 10.1371/journal.pone.0030126
Yang, Tao et al. Gut dysbiosis is linked to hypertension. Hypertension 65, 1331–1340 (2015).
pubmed: 25870193
doi: 10.1161/HYPERTENSIONAHA.115.05315
Baxmann, A. C. et al. Influence of muscle mass and physical activity on serum and urinary creatinine and serum cystatin C. Clin. J. Am. Soc. Nephrol. CJASN 3, 348–354 (2008).
doi: 10.2215/CJN.02870707
Braverman, N. E. & Moser, A. B. Functions of plasmalogen lipids in health and disease. Biochim. Biophys. Acta BBA - Mol. Basis Dis. 1822, 1442–1452 (2012).
doi: 10.1016/j.bbadis.2012.05.008
Merz, B. et al. Dietary pattern and plasma BCAA-variations in healthy men and women-results from the KarMeN study. Nutrients 10, 1–5 (2018).
Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011).
pubmed: 21423183
pmcid: 3126616
doi: 10.1038/nm.2307
Ruiz-Canela, M. et al. Plasma branched-chain amino acids and incident cardiovascular disease in the PREDIMED trial. Clin. Chem. 62, 582–592 (2016).
pubmed: 26888892
pmcid: 4896732
doi: 10.1373/clinchem.2015.251710
Kreisberg, R. A. & Kasim, S. Cholesterol metabolism and aging. Am. J. Med. 82, 54–60 (1987).
pubmed: 3544833
doi: 10.1016/0002-9343(87)90272-5
National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 106, 3143–421 (2002).
Brennan, D. J., O’Connor, D. P., Rexhepaj, E., Ponten, F. & Gallagher, W. M. Antibody-based proteomics: fast-tracking molecular diagnostics in oncology. Nat. Rev. Cancer 10, 605–617 (2010).
pubmed: 20720569
doi: 10.1038/nrc2902
Salvucci, M. et al. System-based approaches as prognostic tools for glioblastoma. BMC Cancer 19, 1092 (2019).
pubmed: 31718568
pmcid: 6852738
doi: 10.1186/s12885-019-6280-2
Dalerba, P. et al. CDX2 as a prognostic biomarker in stage II and stage III colon cancer. N. Engl. J. Med. 374, 211–222 (2016).
pubmed: 26789870
pmcid: 4784450
doi: 10.1056/NEJMoa1506597
Frantzi, M., Bhat, A. & Latosinska, A. Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development. Clin. Transl. Med. 3, 7 (2014).
pubmed: 24679154
pmcid: 3994249
doi: 10.1186/2001-1326-3-7
Borrebaeck, C. A. K. Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer. Nat. Rev. Cancer 17, 199–204 (2017).
pubmed: 28154374
doi: 10.1038/nrc.2016.153
Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).
pubmed: 24097068
pmcid: 3838666
doi: 10.1038/ng.2797
Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).
pubmed: 25673412
pmcid: 4338562
doi: 10.1038/nature14132
Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).
pubmed: 25673413
pmcid: 4382211
doi: 10.1038/nature14177