Personal Dense Dynamic Data Clouds Connect Systems Biomedicine to Scientific Wellness.
Biological aging
Correlation networks
Dense dynamic data clouds
Metabolome
Microbiome
P4 health care
Personalized coaching
Polygenic risk scores
Scientific wellness
Systems biology
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
19
4
2022
pubmed:
20
4
2022
medline:
21
4
2022
Statut:
ppublish
Résumé
The dramatic convergence of molecular biology, genomics, proteomics, metabolomics, bioinformatics, and artificial intelligence has provided a substrate for deep understanding of the biological basis of health and disease. Systems biology is a holistic, dynamic, integrative, cross-disciplinary approach to biological complexity that embraces experimentation, technology, computation, and clinical translation. Systems Medicine integrates genome analyses and longitudinal deep phenotyping with biological pathways and networks to understand mechanisms of disease, identify relevant blood biomarkers, define druggable molecular targets, and enhance the maintenance or restoration of wellness. Two programs initiated our understanding of data-driven population-based wellness. The Pioneer 100 Study of Scientific Wellness and the much larger Arivale commercial program that followed had two spectacular results: demonstrating the feasibility and utility of collecting longitudinal multiomic data, and then generating dense, dynamic data clouds for each individual to utilize actionable metrics for promoting health and preventing disease when combined with personalized coaching. Future developments in these domains will enable better population health and personal, preventive, predictive, participatory (P4) health care.
Identifiants
pubmed: 35437729
doi: 10.1007/978-1-0716-2265-0_15
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
315-334Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Huber M, Knottnerus JA, Green L et al (2011) How should we define health? BMJ 343:d4163. https://doi.org/10.1136/bmj.d4163
doi: 10.1136/bmj.d4163
Conti AA (2018) Historical evolution of the concept of health in Western medicine. Acta Biomed 89(3):352–354. https://doi.org/10.23750/abm.v89i3.6739
doi: 10.23750/abm.v89i3.6739
Declaration of Alma-Ata (1979) Lancet 1(8109):217–218
Price ND, Magis AT, Earls JC et al (2017) A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat Biotechnol 35(8):747–756. https://doi.org/10.1038/nbt.3870
doi: 10.1038/nbt.3870
Chen R, Mias GI, Li-Pook-Than J et al (2012) Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148(6):1293–1307. https://doi.org/10.1016/j.cell.2012.02.009
doi: 10.1016/j.cell.2012.02.009
Smarr L (2012) Quantifying your body: a how-to guide from a systems biology perspective. Biotechnol J 7(8):980–991. https://doi.org/10.1002/biot.201100495
doi: 10.1002/biot.201100495
David LA, Materna AC, Friedman J et al (2014) Host lifestyle affects human microbiota on daily timescales. Genome Biol 15(7):R89. https://doi.org/10.1186/gb-2014-15-7-r89
doi: 10.1186/gb-2014-15-7-r89
Welter D, MacArthur J, Morales J et al (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42(Database Issue):D1001–D1006. https://doi.org/10.1093/nar/gkt1229
doi: 10.1093/nar/gkt1229
Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci U S A 99(12):7821–7826. https://doi.org/10.1073/pnas.122653799
doi: 10.1073/pnas.122653799
Torkamani A, Wineinger NE, Topol EJ (2018) The personal and clinical utility of polygenic risk scores. Nat Rev Genet 19(9):581–590. https://doi.org/10.1038/s41576-018-0018-x
doi: 10.1038/s41576-018-0018-x
Magis AT, Earls JC, Glusman G et al (2018) Reply to “Precision medicine in the clouds”. Nat Biotechnol 36(8):680–682. https://doi.org/10.1038/nbt.4211
doi: 10.1038/nbt.4211
Zubair N, Conomos MP, Hood L et al (2019) Genetic predisposition impacts clinical changes in a lifestyle coaching program. Sci Rep 9(1):6805. https://doi.org/10.1038/s41598-019-43058-0
doi: 10.1038/s41598-019-43058-0
Wainberg M, Magis AT, Earls JC et al (2020) Multiomic blood correlates of genetic risk identify presymptomatic disease alterations. Proc Natl Acad Sci U S A 117(35):21813–21820. https://doi.org/10.1073/pnas.2001429117
doi: 10.1073/pnas.2001429117
Magis AT, Rappaport N, Conomos MP et al (2020) Untargeted longitudinal analysis of a wellness cohort identifies markers of metastatic cancer years prior to diagnosis. Sci Rep 10(1):16275. https://doi.org/10.1038/s41598-020-73451-z
doi: 10.1038/s41598-020-73451-z
Earls JC, Rappaport N, Heath L et al (2019) 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(Suppl 1):S52–S60. https://doi.org/10.1093/gerona/glz220
doi: 10.1093/gerona/glz220
Wilmanski T, Rappaport N, Earls JC et al (2019) Blood metabolome predicts gut microbiome alpha-diversity in humans. Nat Biotechnol 37(10):1217–1228. https://doi.org/10.1038/s41587-019-0233-9
doi: 10.1038/s41587-019-0233-9
Manor O, Zubair N, Conomos MP et al (2018) A multi-omic association study of trimethylamine N-oxide. Cell Rep 24(4):935–946. https://doi.org/10.1016/j.celrep.2018.06.096
doi: 10.1016/j.celrep.2018.06.096
Wilmanski T, Diener C, Rappaport N et al (2021) Gut microbiome pattern reflects healthy ageing and predicts survival in humans. Nat Metab 3(2):274–286. https://doi.org/10.1038/s42255-021-00348-0
doi: 10.1038/s42255-021-00348-0
Levy R, Magis AT, Earls JC et al (2020) Longitudinal analysis reveals transition barriers between dominant ecological states in the gut microbiome. Proc Natl Acad Sci U S A 117(24):13839–13845. https://doi.org/10.1073/pnas.1922498117
doi: 10.1073/pnas.1922498117
Wilmanski T, Rappaport N, Diener C et al (2021) From taxonomy to metabolic output: what factors define gut microbiome health? Gut Microbes 13(1):1–20. https://doi.org/10.1080/19490976.2021.1907270
doi: 10.1080/19490976.2021.1907270
Costello EK, Stagaman K, Dethlefsen L et al (2012) The application of ecological theory toward an understanding of the human microbiome. Science 336(6086):1255–1262. https://doi.org/10.1126/science.1224203
doi: 10.1126/science.1224203
Patnode ML, Beller ZW, Han ND et al (2019) Interspecies competition impacts targeted manipulation of human gut bacteria by fiber-derived glycans. Cell 179(1):59–73.e13. https://doi.org/10.1016/j.cell.2019.08.011
doi: 10.1016/j.cell.2019.08.011
Klemera P, Doubal S (2006) A new approach to the concept and computation of biological age. Mech Ageing Dev 127(3):240–248. https://doi.org/10.1016/j.mad.2005.10.004
doi: 10.1016/j.mad.2005.10.004
Murabito JM, Zhao Q, Larson MG et al (2018) Measures of biologic age in a community sample predict mortality and age-related disease: the Framingham offspring study. J Gerontol A Biol Sci Med Sci 73(6):757–762. https://doi.org/10.1093/gerona/glx144
doi: 10.1093/gerona/glx144
Biagi E, Franceschi C, Rampelli S et al (2016) Gut microbiota and extreme longevity. Curr Biol 26(11):1480–1485. https://doi.org/10.1016/j.cub.2016.04.016
doi: 10.1016/j.cub.2016.04.016
Telomeres Mendelian Randomization C, Haycock PC, Burgess S et al (2017) Association between telomere length and risk of cancer and non-neoplastic diseases: a Mendelian randomization study. JAMA Oncol 3(5):636–651. https://doi.org/10.1001/jamaoncol.2016.5945
doi: 10.1001/jamaoncol.2016.5945
Tebani A, Gummesson A, Zhong W et al (2020) Integration of molecular profiles in a longitudinal wellness profiling cohort. Nat Commun 11(1):4487. https://doi.org/10.1038/s41467-020-18148-7
doi: 10.1038/s41467-020-18148-7
Schussler-Fiorenza Rose SM, Contrepois K, Moneghetti KJ et al (2019) A longitudinal big data approach for precision health. Nat Med 25(5):792–804. https://doi.org/10.1038/s41591-019-0414-6
doi: 10.1038/s41591-019-0414-6
Butte AJ (2017) Big data opens a window onto wellness. Nat Biotechnol 35(8):720–721. https://doi.org/10.1038/nbt.3934
doi: 10.1038/nbt.3934
Diamandis EP (2015) The Hundred Person Wellness Project and Google’s Baseline Study: medical revolution or unnecessary and potentially harmful over-testing? BMC Med 13:5. https://doi.org/10.1186/s12916-014-0239-6
doi: 10.1186/s12916-014-0239-6
Vogt H, Green S, Broderson J (2018) Precision medicine in the clouds. Nat Biotechnol 36(8):678–680. https://doi.org/10.1038/nbt.4210
doi: 10.1038/nbt.4210
Sudlow C, Gallacher J, Allen N et al (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12(3):e1001779. https://doi.org/10.1371/journal.pmed.1001779
doi: 10.1371/journal.pmed.1001779
Kurakin A, Bredesen DE (2020) Alzheimer’s disease as a systems network disorder: chronic stress/dyshomeostasis, innate immunity, and genetics. Aging (Albany NY) 12(18):17815–17844. https://doi.org/10.18632/aging.103883
doi: 10.18632/aging.103883
Theendakara V, Patent A, Peters Libeu CA et al (2013) Neuroprotective Sirtuin ratio reversed by ApoE4. Proc Natl Acad Sci U S A 110(45):18303–18308. https://doi.org/10.1073/pnas.1314145110
doi: 10.1073/pnas.1314145110
Rosenberg A, Mangialasche F, Ngandu T et al (2020) Multidomain interventions to prevent cognitive impairment, Alzheimer’s disease, and dementia: from FINGER to world-wide FINGERS. J Prev Alzheimers Dis 7(1):29–36. https://doi.org/10.14283/jpad.2019.41
doi: 10.14283/jpad.2019.41
Rebok GW, Ball K, Guey LT et al (2014) Ten-year effects of the advanced cognitive training for independent and vital elderly cognitive training trial on cognition and everyday functioning in older adults. J Am Geriatr Soc 62(1):16–24. https://doi.org/10.1111/jgs.12607
doi: 10.1111/jgs.12607
Dzau VJ, Inouye SK, Rowe JW et al (2019) Enabling healthful aging for all - The National Academy of Medicine grand challenge in healthy longevity. N Engl J Med 381(18):1699–1701. https://doi.org/10.1056/NEJMp1912298
doi: 10.1056/NEJMp1912298
Omenn G (2006) Grand challenges and great opportunities in science, technology, and public policy. Science 314:1696. https://doi.org/10.1126/science.1135003
doi: 10.1126/science.1135003
Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372(9):793–795. https://doi.org/10.1056/NEJMp1500523
doi: 10.1056/NEJMp1500523