Abstract
Regardless of chosen definitions, health, as well as disease, are in their essence only labels describing two enormous groups of phenotypes. Thus, to study either health or disease, we need to understand the factors affecting the development of specific phenotypes. The current paradigm used to explain the development of phenotypes oversimplifies the entire process by omitting the key factor of time. The novel concept of the dynamic pathosome aims to provide a much more detailed picture than the current paradigm. It provides a novel theoretical framework as well as practical applications, but most importantly can change how we perceive health and disease. This chapter reviews the concept of the dynamic pathosome and the benefits it could offer to biomedicine.
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References
Adams DC, Collyer ML (2009) A general framework for the analysis of phenotypic trajectories in evolutionary studies. Evolution 63:1143–1154. https://doi.org/10.1111/j.1558-5646.2009.00649.x
Ahadi S, Zhou W, Schüssler-Fiorenza Rose SM, Sailani MR, Contrepois K, Avina M, Ashland M, Brunet A, Snyder M (2020) Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat Med 26:83–90. https://doi.org/10.1038/s41591-019-0719-5
Beery AK (2018) Inclusion of females does not increase variability in rodent research studies. Curr Opin Behav Sci Sex Gender 23:143–149. https://doi.org/10.1016/j.cobeha.2018.06.016
Bennette C, Vickers A (2012) Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents. BMC Med Res Methodol 12:21. https://doi.org/10.1186/1471-2288-12-21
Beom J, Woo EJ, Lee IS, Kim MJ, Kim Y-S, Oh CS, Lee S-S, Lim SB, Shin DH (2014) Harris lines observed in human skeletons of Joseon Dynast Korea. Anat Cell Biol 47:66–72. https://doi.org/10.5115/acb.2014.47.1.66
Bienertová-Vašků J, Zlámal F, Nečesánek I, Konečný D, Vasku A (2016) Calculating stress: from entropy to a thermodynamic concept of health and disease. PLoS ONE 11:e0146667. https://doi.org/10.1371/journal.pone.0146667
Bunning BJ, Contrepois K, Lee-McMullen B, Dhondalay GKR, Zhang W, Tupa D, Raeber O, Desai M, Nadeau KC, Snyder MP, Andorf S (2019) Global metabolic profiling to model biological processes of aging in twins. Aging Cell e13073. https://doi.org/10.1111/acel.13073
Burg G (2019) Changes in color of the skin and systemic disease. Clin Dermatol https://doi.org/10.1016/j.clindermatol.2019.07.033
Charalampopoulos D, McLoughlin A, Elks CE, Ong KK (2014) Age at menarche and risks of all-cause and cardiovascular death: a systematic review and meta-analysis. Am J Epidemiol 180:29–40. https://doi.org/10.1093/aje/kwu113
Cho C, Cho E, Kim N, Shin J, Woo S, Lee E, Hwang J, Ha J (2019) Age-related biophysical changes of the epidermal and dermal skin in Korean women. Skin Res Technol 25:504–511. https://doi.org/10.1111/srt.12679
Crawford K, Calo R (2016) There is a blind spot in AI research. Nat News 538:311. https://doi.org/10.1038/538311a
De Lorenzo A, Gratteri S, Gualtieri P, Cammarano A, Bertucci P, Di Renzo L (2019) Why primary obesity is a disease? J Transl Med 17. https://doi.org/10.1186/s12967-019-1919-y
Demirovic D, Rattan SIS (2013) Establishing cellular stress response profiles as biomarkers of homeodynamics, health and hormesis. Exp Gerontol 48:94–98. https://doi.org/10.1016/j.exger.2012.02.005
Doherty A, Kernogitski Y, Kulminski AM, de Magalhães JP (2017) Identification of polymorphisms in cancer patients that differentially affect survival with age. Aging 9:2117–2136. https://doi.org/10.18632/aging.101305
Dvornyk V, Waqar-ul-Haq (2012) Genetics of age at menarche: a systematic review. Hum Reprod Update 18:198–210. https://doi.org/10.1093/humupd/dmr050
Ereshefsky M (2009) Defining ‘health’ and ‘disease.’ H Stud Hist Philos Sci Part C Stud Hist Philos Biol Biomed Sci 40:221–227. https://doi.org/10.1016/j.shpsc.2009.06.005
Eykholt K, Evtimov I, Fernandes E, Li B, Rahmati A, Xiao C, Prakash A, Kohno T, Song DX (2018) Robust physical-world attacks on deep learning visual classification. In: 2018 IEEECVF conference computer society conference on computer vision and pattern recognition, pp 1625–1634. https://doi.org/10.1109/cvpr.2018.00175
Finlayson SG, Bowers JD, Ito J, Zittrain JL, Beam AL, Kohane IS (2019) Adversarial attacks on medical machine learning. Science 363:1287–1289. https://doi.org/10.1126/science.aaw4399
Garcia M (2016) Racist in the machine: the disturbing implications of algorithmic bias. World Policy J 33:111–117
Garovic VD, August P (2013) Preeclampsia and the future risk of hypertension: the pregnant evidence. Curr Hypertens Rep 15. https://doi.org/10.1007/s11906-013-0329-4
Gill D, Brewer CF, Sivakumaran P, Bowden J, Sheehan NA, Minelli C (2018) Age at menarche and adult body mass index: a Mendelian randomization study. Int J Obes 1. https://doi.org/10.1038/s41366-018-0048-7
Gonsiorek J (1991) The empirical basis for the demise of the illness model of homosexuality. In: Homosexuality: research implications for public policy. SAGE Publications, Inc., Thousand Oaks, pp 115–136. https://doi.org/10.4135/9781483325422
Grandjean P (2008) Late insights into early origins of disease. Basic Clin Pharmacol Toxicol 102:94–99. https://doi.org/10.1111/j.1742-7843.2007.00167.x
Harris SE, Riggio V, Evenden L, Gilchrist T, McCafferty S, Murphy L, Wrobel N, Taylor AM, Corley J, Pattie A, Cox SR, Martin-Ruiz C, Prendergast J, Starr JM, Marioni RE, Deary IJ (2017) Age-related gene expression changes, and transcriptome wide association study of physical and cognitive aging traits, in the Lothian Birth Cohort 1936. Aging 9:2489–2503. https://doi.org/10.18632/aging.101333
Hesslow G (1993) Do we need a concept of disease? Theor Med 14:1–14. https://doi.org/10.1007/bf00993984
Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14:R115. https://doi.org/10.1186/gb-2013-14-10-r115
Huan T, Chen G, Liu C, Bhattacharya A, Rong J, Chen BH, Seshadri S, Tanriverdi K, Freedman JE, Larson MG, Murabito JM, Levy D (2018) Age‐associated microRNA expression in human peripheral blood is associated with all‐cause mortality and age‐related traits. Aging Cell 17. https://doi.org/10.1111/acel.12687
Hwang AE, Mack TM, Hamilton AS, James Gauderman W, Bernstein L, Cockburn MG, Zadnick J, Rand KA, Hopper JL, Cozen W (2013) Childhood Infections and adult height in monozygotic twin pairs. Am J Epidemiol 178:551–558. https://doi.org/10.1093/aje/kwt012
Ioannidis JPA (2005) Why most published research findings are false. PLOS Med. 2:e124. https://doi.org/10.1371/journal.pmed.0020124
Iorio MV, Ferracin M, Liu C-G, Veronese A, Spizzo R, Sabbioni S, Magri E, Pedriali M, Fabbri M, Campiglio M, Ménard S, Palazzo JP, Rosenberg A, Musiani P, Volinia S, Nenci I, Calin GA, Querzoli P, Negrini M, Croce CM (2005) MicroRNA gene expression deregulation in human breast cancer. Cancer Res 65:7065–7070. https://doi.org/10.1158/0008-5472.CAN-05-1783
Jackson SJ, Andrews N, Ball D, Bellantuono I, Gray J, Hachoumi L, Holmes A, Latcham J, Petrie A, Potter P, Rice A, Ritchie A, Stewart M, Strepka C, Yeoman M, Chapman K (2017) Does age matter? The impact of rodent age on study outcomes. Lab Anim 51:160–169. https://doi.org/10.1177/0023677216653984
Jones OR, Scheuerlein A, Salguero-Gómez R, Camarda CG, Schaible R, Casper BB, Dahlgren JP, Ehrlén J, García MB, Menges ES, Quintana-Ascencio PF, Caswell H, Baudisch A, Vaupel JW (2014) Diversity of ageing across the tree of life. Nature 505:169. https://doi.org/10.1038/nature12789
Julia Angwin JL (2016) Machine Bias [WWW Document]. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. Accessed 11.12.19.
Jung RT (1997) Obesity as a disease. Br Med Bull 53:307–321. https://doi.org/10.1093/oxfordjournals.bmb.a011615
Kilkenny C, Browne WJ, Cuthill IC, Emerson M, Altman DG (2010) Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS Biol 8. https://doi.org/10.1371/journal.pbio.1000412
Lehallier B, Gate D, Schaum N, Nanasi T, Lee SE, Yousef H, Moran Losada P, Berdnik D, Keller A, Verghese J, Sathyan S, Franceschi C, Milman S, Barzilai N, Wyss-Coray T (2019) Undulating changes in human plasma proteome profiles across the lifespan. Nat Med 25:1843–1850. https://doi.org/10.1038/s41591-019-0673-2
Lenart P, Scheringer M, Bienertova-Vasku J (2019) The pathosome: a dynamic three-dimensional view of disease-environment interaction. BioEssays 41:1900014. https://doi.org/10.1002/bies.201900014
de Magalhães JP (2013) How ageing processes influence cancer. Nat Rev Cancer 13:357–365. https://doi.org/10.1038/nrc3497
Martinez-Gonzalez I, Ghaedi M, Steer CA, Mathä L, Vivier E, Takei F (2018) ILC2 memory: recollection of previous activation. Immunol Rev 283:41–53. https://doi.org/10.1111/imr.12643
Martı́nez DE (1998) Mortality patterns suggest lack of senescence in hydra. Exp Gerontol 33:217–225. https://doi.org/10.1016/S0531-5565(97)00113-7
McEwen BS, Stellar E (1993) Stress and the individual: mechanisms leading to disease. Arch Intern Med 153:2093–2101. https://doi.org/10.1001/archinte.1993.00410180039004
Morris DH, Jones ME, Schoemaker MJ, Ashworth A, Swerdlow AJ (2010) Determinants of age at menarche in the UK: analyses from the breakthrough generations study. Br J Cancer 103:1760–1764. https://doi.org/10.1038/sj.bjc.6605978
Naik S, Larsen SB, Gomez NC, Alaverdyan K, Sendoel A, Yuan S, Polak L, Kulukian A, Chai S, Fuchs E (2017) Inflammatory memory sensitizes skin epithelial stem cells to tissue damage. Nature 550:475–480. https://doi.org/10.1038/nature24271
Nguyen A, Yosinski J, Clune J (2015) Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Presented at the proceedings of the IEEE conference on computer vision and pattern recognition, pp 427–436
North BJ, Sinclair DA (2012) The intersection between aging and cardiovascular disease. Circ Res 110:1097–1108. https://doi.org/10.1161/CIRCRESAHA.111.246876
Rattan SIS (2006) Theories of biological aging: genes, proteins, and free radicals. Free Radic Res 40:1230–1238. https://doi.org/10.1080/10715760600911303
Regan TD, Norton SA (2004) The scarring mechanism of smallpox. J Am Acad Dermatol 50:591–594. https://doi.org/10.1016/j.jaad.2003.10.672
Robb J, Bigazzi R, Lazzarini L, Scarsini C, Sonego F (2001) Social “status” and biological “status”: a comparison of grave goods and skeletal indicators from pontecagnano. Am J Phys Anthropol 115:213–222. https://doi.org/10.1002/ajpa.1076
Ruby JG, Smith M, Buffenstein R (2018) Naked mole-rat mortality rates defy Gompertzian laws by not increasing with age. eLife 7:e31157. https://doi.org/10.7554/eLife.31157
Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1:206–215. https://doi.org/10.1038/s42256-019-0048-x
Sadler KE, Gartland NM, Cavanaugh JE, Kolber BJ (2017) Central amygdala activation of extracellular signal-regulated kinase 1 and age-dependent changes in inflammatory pain sensitivity in mice. Neurobiol Aging 56:100–107. https://doi.org/10.1016/j.neurobiolaging.2017.04.010
Sandelowski M, Voils CI, Knafl G (2009) On quantitizing. J Mix Methods Res 3:208. https://doi.org/10.1177/1558689809334210
Schaible R, Scheuerlein A, Dańko MJ, Gampe J, Martínez DE, Vaupel JW (2015) Constant mortality and fertility over age in Hydra. Proc Natl Acad Sci 112:15701–15706. https://doi.org/10.1073/pnas.1521002112
Sempere LF, Christensen M, Silahtaroglu A, Bak M, Heath CV, Schwartz G, Wells W, Kauppinen S, Cole CN (2007) Altered MicroRNA expression confined to specific epithelial cell subpopulations in breast cancer. Cancer Res 67:11612–11620. https://doi.org/10.1158/0008-5472.CAN-07-5019
Shamir R, Klein C, Amar D, Vollstedt E-J, Bonin M, Usenovic M, Wong YC, Maver A, Poths S, Safer H, Corvol J-C, Lesage S, Lavi O, Deuschl G, Kuhlenbaeumer G, Pawlack H, Ulitsky I, Kasten M, Riess O, Brice A, Peterlin B, Krainc D (2017) Analysis of blood-based gene expression in idiopathic Parkinson disease. Neurology 89:1676–1683. https://doi.org/10.1212/WNL.0000000000004516
Sterling P, Eyer J (1988) Allostasis: a new paradigm to explain arousal pathology. In: Handbook of life stress, cognition and health. Wiley, Oxford, England, pp 629–649
Stöckl D, Meisinger C, Peters A, Thorand B, Huth C, Heier M, Rathmann W, Kowall B, Stöckl H, Döring A (2011) Age at menarche and its association with the metabolic syndrome and its components: results from the KORA F4 study. PLoS ONE 6. https://doi.org/10.1371/journal.pone.0026076
Taslim C, Weng DY, Brasky TM, Dumitrescu RG, Huang K, Kallakury BVS, Krishnan S, Llanos AA, Marian C, McElroy J, Schneider SS, Spear SL, Troester MA, Freudenheim JL, Geyer S, Shields PG (2016) Discovery and replication of microRNAs for breast cancer risk using genome-wide profiling. Oncotarget 7:86457–86468. https://doi.org/10.18632/oncotarget.13241
Varley JM (2003) Germline TP53 mutations and Li-Fraumeni syndrome. Hum Mutat 21:313–320. https://doi.org/10.1002/humu.10185
Voelkl B, Vogt L, Sena ES, Würbel H (2018) Reproducibility of preclinical animal research improves with heterogeneity of study samples. PLoS Biol. 16. https://doi.org/10.1371/journal.pbio.2003693
Wadhwa PD, Buss C, Entringer S, Swanson JM (2009) Developmental origins of health and disease: brief history of the approach and current focus on epigenetic mechanisms. Semin Reprod Med 27:358–368. https://doi.org/10.1055/s-0029-1237424
Wistrand PJ, Stjernschantz J, Olsson K (1997) The incidence and time-course of latanoprost-induced iridial pigmentation as a function of eye color. Surv Ophthalmol 41:S129–S138. https://doi.org/10.1016/S0039-6257(97)80020-3
Yang L, Wang J, Li J, Zhang H, Guo S, Yan M, Zhu Z, Lan B, Ding Y, Xu M, Li W, Gu X, Qi C, Zhu H, Shao Z, Liu B, Tao S-C (2016) Identification of serum biomarkers for gastric cancer diagnosis using a human proteome microarray. Mol Cell Proteomics 15:614–623. https://doi.org/10.1074/mcp.M115.051250
Yokota T, Mishra M, Akatsu H, Tani Y, Miyauchi T, Yamamoto T, Kosaka K, Nagai Y, Sawada T, Heese K (2006) Brain site-specific gene expression analysis in Alzheimer’s disease patients. Eur J Clin Invest 36:820–830. https://doi.org/10.1111/j.1365-2362.2006.01722.x
Zador AM (2019) A critique of pure learning and what artificial neural networks can learn from animal brains. Nat Commun 10:1–7. https://doi.org/10.1038/s41467-019-11786-6
Zlámal F, Lenart P, Kuruczová D, Kalina T, de la Torre G, Ramallo MA, Bienertová-Vašků J (2018) Stress entropic load: new stress measurement method? PLoS ONE 13:e0205812. https://doi.org/10.1371/journal.pone.0205812
Acknowledgements
The project was supported by the CETOCOEN PLUS (CZ.02.1.01/0.0/0.0/15_003/0000469) project of the Ministry of Education, Youth and Sports of the Czech Republic. The project was also supported by the RECETOX Research Infrastructure (LM2015051 and CZ.02.1.01/0.0/0.0/16_013/0001761). Furthermore, Peter Lenart received support from the Brno Ph.D. Talent competition.
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Lenart, P., Scheringer, M., Bienertová-Vašků, J. (2020). The Dynamic Pathosome: A Surrogate for Health and Disease. In: Sholl, J., Rattan, S.I. (eds) Explaining Health Across the Sciences. Healthy Ageing and Longevity, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-52663-4_16
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