Diversidad de perfiles reactivos hemodinámicos entre personas: Implicaciones psicosociales para la medicina personalizada

dc.contributor.authorGandarillas Solinis, Miguel Ángel
dc.contributor.authorGoswami, Nandu
dc.date.accessioned2023-06-22T12:45:32Z
dc.date.available2023-06-22T12:45:32Z
dc.date.issued2022
dc.descriptionTraducción al español del original en inglés (Gandarillas & Goswami, 2022).
dc.description.abstractResumen: Este estudio analizó las diferencias individuales en los patrones de tiempo hemodinámicos y la reactividad a las tareas cognitivas y emocionales, y exploró la diversidad de perfiles psicofisiológicos que podrían utilizarse para la predicción personalizada de diferentes enfermedades. Se llevó a cabo un análisis de los patrones de relación entre la frecuencia cardíaca (FC) y la presión arterial (PA) a lo largo del tiempo mediante correlaciones cruzadas (CC) durante una tarea matemática lógica y una tarea que recordaba emociones negativas (rumiación) en un entorno de laboratorio con 45 participantes. Los resultados mostraron CC máximos de HR-BP durante la tarea matemática significativamente más positivos que los CC máximos de HR-BP durante la tarea de rumiación. Además, nuestros resultados mostraron una gran variedad de perfiles de reactividad hemodinámica entre los participantes, incluso cuando realizaban las mismas tareas. El tipo más frecuente mostró CC de FC-PA positivas bajo actividad cognitiva, y varios ciclos de CC de FC-PA positivos-negativos bajo actividad emocional negativa. En términos generales, nuestros resultados apoyaron la hipótesis principal. Observamos algunas "estrategias de coordinación" distintas basadas en el tiempo en la reactividad del sistema nervioso autónomo bajo carga emocional versus cognitiva. En general, se observaron grandes especificidades individuales, así como situacionales, en los patrones de tiempo de reactividad hemodinámica. Se discuten las posibles relaciones entre esta variedad de perfiles y diferentes características psicosociales, y el potencial para la salud predictiva integradora dentro de la provisión de una medicina altamente personalizada.
dc.description.abstractThis study analyzed the individual differences in hemodynamic time patterns and reactivity to cognitive and emotional tasks, and explored the diversity of psycho-physiological profiles that could be used for the personalized prediction of different diseases. An analysis of heart rate (HR)—blood pressure (BP) relationship patterns across time using cross-correlations (CCs) during a logical-mathematical task and a task recalling negative emotions (rumination) was carried out in a laboratory setting on 45 participants. The results showed maximum HR–BP CCs during the mathematical task significantly more positive than the maximum HR–BP CCs during the rumination task. Furthermore, our results showed a large variety of hemodynamic reactivity profiles across the participants, even when carrying out the same tasks. The most frequent type showed positive HR–BP CCs under cognitive activity, and several positive–negative HR–BP CCs cycles under negative emotional activity. In general terms, our results supported the main hypothesis. We observed some distinct time-based “coordination strategies” in the reactivity of the autonomic nervous system under emotional vs. cognitive loading. Overall, large individual, as well as situational, specificities in hemodynamic reactivity time patterns were seen. The possible relationships between this variety of profiles and different psychosocial characteristics, and the potential for integrative predictive health within the provision of highly personalized medicine, are discussed.
dc.description.departmentDepto. de Psicología Social, del Trabajo y Diferencial
dc.description.facultyFac. de Psicología
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/77240
dc.identifier.doi10.3390/jcm11133869
dc.identifier.issn2077-0383
dc.identifier.officialurlhttps://www.mdpi.com/journal/jcm
dc.identifier.relatedurlhttps://doi.org/10.3390/jcm11133869
dc.identifier.urihttps://hdl.handle.net/20.500.14352/73142
dc.issue.number13
dc.journal.titleJournal of Clinical Medicine
dc.language.isospa
dc.page.initial3869
dc.publisherMDPI
dc.rights.accessRightsopen access
dc.subject.cdu316.6
dc.subject.cdu159.91
dc.subject.keywordSistema nervioso autónomo
dc.subject.keywordHemodinámica
dc.subject.keywordSalud predictiva
dc.subject.keywordMedicina personalizada
dc.subject.keywordFactores psicosociales
dc.subject.keywordMedicina integrativa
dc.subject.keywordAutonomic nervous system
dc.subject.keywordHemodynamics
dc.subject.keywordPredictive health
dc.subject.keywordPersonalized medicine
dc.subject.keywordPsychosocial factors
dc.subject.keywordIntegrative medicine
dc.subject.ucmPsicología social (Psicología)
dc.subject.ucmMedicina psicosomática
dc.subject.unesco6114 Psicología Social
dc.titleDiversidad de perfiles reactivos hemodinámicos entre personas: Implicaciones psicosociales para la medicina personalizada
dc.title.alternativeDiversity of Hemodynamic Reactive Profiles across Persons—Psychosocial Implications for Personalized Medicine. Journal of Clinical Medicine
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number11
dcterms.referencesCalhoun, D.A.; Oparil, S. Hypertension and Sympathetic Nervous System Activity. In Primer on the Autonomic Nervous System; Academic Press: London, UK, 2004; pp. 241–244. [Google Scholar] [CrossRef] Gandarillas, M.A.; Câmara, S.G.; Scarparo, H. Estressores sociais da hipertensão em comunidades carentes. Psicol. Reflexão E Crítica 2005, 18, 62–71. [Google Scholar] [CrossRef][Green Version] Ottaviani, C.; Brosschot, J.F.; Lonigro, A.; Medea, B.; Diest, I.V.; Thayer, J.F. Hemodynamic Profiles of Functional and Dysfunctional Forms of Repetitive Thinking. Ann. Behav. Med. 2016, 51, 261–271. [Google Scholar] [CrossRef] [PubMed][Green Version] Walther, L.M.; Von Känel, R.; Heimgartner, N.; Zuccarella-Hackl, C.; Ehlert, U.; Wirtz, P.H. Altered Cardiovascular Reactivity to and Recovery from Cold Face Test-Induced Parasympathetic Stimulation in Essential Hypertension. J. Clin. Med. 2021, 10, 2714. [Google Scholar] [CrossRef] [PubMed] Davydov, D.M.; Shapiro, D.; Cook, I.A.; Goldstein, I. Baroreflex mechanisms in major depression. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2007, 31, 164–177. [Google Scholar] [CrossRef][Green Version] Krantz, D.S.; Mcceney, M.K. Effects of Psychological and Social Factors on Organic Disease: A Critical Assessment of Research on Coronary Heart Disease. Annu. Rev. Psychol. 2002, 53, 341–369. [Google Scholar] [CrossRef][Green Version] Wood, S.K.; Bhatnagar, S. Resilience to the effects of social stress: Evidence from clinical and preclinical studies on the role of coping strategies. Neurobiol. Stress 2015, 1, 164–173. [Google Scholar] [CrossRef][Green Version] Westhoff-Bleck, M.; Lemke, L.H.; Bleck, J.M.S.; Bleck, A.C.; Bauersachs, J.; Kahl, K.G. Depression Associated with Reduced Heart Rate Variability Predicts Outcome in Adult Congenital Heart Disease. J. Clin. Med. 2021, 10, 1554. [Google Scholar] [CrossRef] Brown, E.G.; Gallagher, S.; Creaven, A.M. Loneliness and acute stress reactivity: A systematic review of psychophysiological studies. Psychophysiology 2017, 55, e13031. [Google Scholar] [CrossRef] Lee, E.M.; Hughes, B.M. Trait dominance is associated with vascular cardiovascular responses, and attenuated habituation, to social stress. Int. J. Psychophysiol. 2014, 92, 79–84. [Google Scholar] [CrossRef] Ottaviani, C.; Shapiro, D.; Fitzgerald, L. Rumination in the laboratory: What happens when you go back to everyday life? Psychophysiology 2010, 48, 453–461. [Google Scholar] [CrossRef][Green Version] Shioiri, T. Momentary changes in the cardiovascular autonomic system during mental loading in patients with panic disorder: A new physiological index. J. Affect. Disord. 2004, 82, 395–401. [Google Scholar] [CrossRef] [PubMed] Ziegler, M.G. Psychological Stress and the Autonomic Nervous System. In Primer on the Autonomic Nervous System; Academic Press: London, UK, 2004; pp. 189–190. [Google Scholar] [CrossRef] Pieritz, K.; Süssenbach, P.; Rief, W.; Euteneuer, F. Subjective Social Status and Cardiovascular Reactivity: An Experimental Examination. Front. Psychol. 2016, 7, 1091. [Google Scholar] [CrossRef] [PubMed][Green Version] Schwerdtfeger, A.; Gaisbachgrabner, K.; Traunmüller, C. Life Satisfaction and Hemodynamic Reactivity to Mental Stress. Ann. Behav. Med. 2016, 51, 464–469. [Google Scholar] [CrossRef] [PubMed] Grote, V.; Kelz, C.; Goswami, N.; Stossier, H.; Tafeit, E.; Moser, M. Cardio-autonomic control and wellbeing due to oscillating color light exposure. Physiol. Behav. 2013, 114–115, 55–64. [Google Scholar] [CrossRef] Buckwalter, J.; Rizzo, A.; John, B.; Finlay, L.; Wong, A.; Chin, E.; Seeman, T. Analyzing the impact of stress: A comparison between a factor analytic and a composite measurement of allostatic load. In Proceedings of the Interservice/Industry Training, Simulation & Education Conference (I/ITSEC), Orlando, FL, USA, 28 November–1 December 2011. [Google Scholar] Chen, Z.; Purdon, P.L.; Brown, E.N.; Barbieri, R. A Unified Point Process Probabilistic Framework to Assess Heartbeat Dynamics and Autonomic Cardiovascular Control. Front. Physiol. 2012, 3, 4. [Google Scholar] [CrossRef][Green Version] Dan-Glauser, E.S.; Gross, J.J. Emotion regulation and emotion coherence: Evidence for strategy-specific effects. Emotion 2013, 13, 832–842. [Google Scholar] [CrossRef] Gong, W.; Wang, S. Support Vector Machine for Assistant Clinical Diagnosis of Cardiac Disease. In Proceedings of the 2009 WRI Global Congress on Intelligent Systems, Xiamen, China, 19–21 May 2009. [Google Scholar] [CrossRef] Huang, S.; Shen, Q.; Duong, T.Q. Artificial Neural Network Prediction of Ischemic Tissue Fate in Acute Stroke Imaging. J. Cereb. Blood Flow Metab. 2010, 30, 1661–1670. [Google Scholar] [CrossRef][Green Version] Iervasi, G.; Franchi, D. A new Web-based medical tool for assessment and prevention of comprehensive cardiovascular risk. Ther. Clin. Risk Manag. 2011, 7, 59. [Google Scholar] [CrossRef][Green Version] Ilies, R.; Dimotakis, N.; Watson, D. Mood, blood pressure, and heart rate at work: An experience-sampling study. J. Occup. Health Psychol. 2010, 15, 120–130. [Google Scholar] [CrossRef] Olbrich, S.; Sander, C.; Matschinger, H.; Mergl, R.; Trenner, M.; Schönknecht, P.; Hegerl, U. Brain and Body. J. Psychophysiol. 2011, 25, 190–200. [Google Scholar] [CrossRef] Salem, A.B.M.; Revett, K.; El-Dahshan, E.S.A. Machine learning in electrocardiogram diagnosis. In Proceedings of the 2009 International Multiconference on Computer Science and Information Technology, Mragowo, Poland, 12–14 October 2009. [Google Scholar] [CrossRef] García, A.O.M.; Müller, M.F.; Schindler, K.; Rummel, C. Genuine cross-correlations: Which surrogate based measure reproduces analytical results best? Neural Netw. 2013, 46, 154–164. [Google Scholar] [CrossRef] [PubMed] Hussain, S.; Raza, Z.; Giacomini, G.; Goswami, N. Support Vector Machine-Based Classification of Vasovagal Syncope Using Head-Up Tilt Test. Biology 2021, 10, 1029. [Google Scholar] [CrossRef] [PubMed] Seliger, A.; Hansen, L.B. Characterization and Discrimination of Pathological Electrocardiograms Using Advanced Machine Learning Methods; Technical University of Denmark: Lyngby, Denmark, 2013. [Google Scholar] Goswami, N.; Roessler, A.; Lackner, H.K.; Schneditz, D.; Grasser, E.; Hinghofer-Szalkay, H.G. Heart rate and stroke volume response patterns to augmented orthostatic stress. Clin. Auton. Res. 2009, 19, 157–165. [Google Scholar] [CrossRef] [PubMed] Papousek, I.; Nauschnegg, K.; Paechter, M.; Lackner, H.K.; Goswami, N.; Schulter, G. Trait and state positive affect and cardiovascular recovery from experimental academic stress. Biol. Psychol. 2010, 83, 108–115. [Google Scholar] [CrossRef] Schlotz, W.; Kumsta, R.; Layes, I.; Entringer, S.; Jones, A.; Wüst, S. Covariance Between Psychological and Endocrine Responses to Pharmacological Challenge and Psychosocial Stress: A Question of Timing. Psychosom. Med. 2008, 70, 787–796. [Google Scholar] [CrossRef] Decaro, J.A. Beyond catecholamines: Measuring autonomic responses to psychosocial context. Am. J. Hum. Biol. 2015, 28, 309–317. [Google Scholar] [CrossRef] Katz, L.F. Domestic violence and vagal reactivity to peer provocation. Biol. Psychol. 2007, 74, 154–164. [Google Scholar] [CrossRef][Green Version] Kemp, A.H.; Arias, J.A.; Fisher, Z. Social Ties, Health and Wellbeing: A Literature Review and Model. In Neuroscience and Social Science; Springer: Cham, Switzerland, 2017; pp. 397–427. [Google Scholar] Porges, S.W.; Furman, S.A. The early development of the autonomic nervous system provides a neural platform for social behaviour: A polyvagal perspective. Infant Child Dev. 2010, 20, 106–118. [Google Scholar] [CrossRef][Green Version] Seery, M.D. The Biopsychosocial Model of Challenge and Threat: Using the Heart to Measure the Mind. Soc. Personal. Psychol. Compass 2013, 7, 637–653. [Google Scholar] [CrossRef] Tsai, J.L.; Levenson, R.W.; Mccoy, K. Cultural and temperamental variation in emotional response. Emotion 2006, 6, 484–497. [Google Scholar] [CrossRef][Green Version] Kennedy, A.E.; Rubin, K.H.; Hastings, P.D.; Maisel, B. Longitudinal relations between child vagal tone and parenting behavior: 2 to 4 years. Dev. Psychobiol. 2004, 45, 10–21. [Google Scholar] [CrossRef] [PubMed] Levenson, R.W. Autonomic Nervous System Differences among Emotions. Psychol. Sci. 1992, 3, 23–27. [Google Scholar] [CrossRef] Pavlov, S.V.; Reva, N.V.; Loktev, K.V.; Tumyalis, A.V.; Korenyok, V.V.; Aftanas, L.I. The temporal dynamics of cognitive reappraisal: Cardiovascular consequences of downregulation of negative emotion and upregulation of positive emotion. Psychophysiology 2013, 51, 178–186. [Google Scholar] [CrossRef] [PubMed] Richards, M.; Eves, F.F. Personality, temperament and the cardiac defense response. Personal. Individ. Differ. 1991, 12, 999–1007. [Google Scholar] [CrossRef] Walters, R.P.; Harrison, P.K.; DeVore, B.B.; Harrison, D.W. Capacity theory: A neuropsychological perspective on shared neural systems regulating hostile violence prone behavior and the metabolic syndrome. J. Neurol. Disord. Epilepsy 2016, 3, 1014–1030. [Google Scholar] Cacioppo, J.T. Social neuroscience: Autonomic, neuroendocrine, and immune responses to stress. Psychophysiology 1994, 31, 113–128. [Google Scholar] [CrossRef] Cacioppo, J.T. Feelings and emotions: Roles for electrophysiological markers. Biol. Psychol. 2004, 67, 235–243. [Google Scholar] [CrossRef] [PubMed] Gray, J.A. The psychophysiological basis of introversion-extraversion. Behav. Res. Ther. 1970, 8, 249–266. [Google Scholar] [CrossRef] Gray, J.A. A Critique of Eysenck’s Theory of Personality. In A Model for Personality; Springer: Berlin/Heidelberg, Germany, 1981; pp. 246–276. [Google Scholar] [CrossRef] Fowles, D.C. The Three Arousal Model: Implications of Grays Two-Factor Learning Theory for Heart Rate, Electrodermal Activity, and Psychopathy. Psychophysiology 1980, 17, 87–104. [Google Scholar] [CrossRef] [PubMed] Kemper, T.D. How Many Emotions Are There? Wedding the Social and the Autonomic Components. Am. J. Sociol. 1987, 93, 263–289. [Google Scholar] [CrossRef] Norris, C.J.; Chen, E.E.; Zhu, D.C.; Small, S.L.; Cacioppo, J.T. The Interaction of Social and Emotional Processes in the Brain. J. Cogn. Neurosci. 2004, 16, 1818–1829. [Google Scholar] [CrossRef] [PubMed][Green Version] Perris, C. A theoretical framework for linking the experience of dysfunctional parental rearing attitudes with manifest psychopathology. Acta Psychiatr. Scand. 1988, 78, 93–109. [Google Scholar] [CrossRef] [PubMed] Schwartz, G.E. Emotion and psychophysiological organization: A systems approach. In Psychophysiology: Systems, Processes, and Applications; Coles, G.H., Donchin, E., Porges, S.W., Eds.; Elsevier: Amsterdam, The Netherlands, 1986; pp. 354–377. [Google Scholar] Yuenyongchaiwat, K.; Sheffield, D.; Baker, I.; Maratos, F. Hemodynamic responses to active and passive coping tasks and the prediction of future blood pressure in Thai participants: A preliminary prospective cohort study. Jpn. Psychol. Res. 2015, 57, 288–299. [Google Scholar] [CrossRef][Green Version] Gandarillas, M.Á. Psychosocial correlates of peripheral vegetative activity and coordination. Aletheia 2011, 35–36, 211–230. [Google Scholar] Goswami, N.; Lackner, H.K.; Grasser, E.K.; Hinghofer-Szalkay, H.G. Individual stability of orthostatic tolerance response. Acta Physiol. Hung. 2009, 96, 157–166. [Google Scholar] [CrossRef] Beauchaine, T.P.; Gatzke-Kopp, L.; Mead, H.K. Polyvagal Theory and developmental psychopathology: Emotion dysregulation and conduct problems from preschool to adolescence. Biol. Psychol. 2007, 74, 174–184. [Google Scholar] [CrossRef][Green Version] Hall, J.E.; Guyton, A.C. Guyton and Hall Textbook of Medical Physiology; Elsevier: Philadelphia, PA, USA, 2016. [Google Scholar] Raven, P.B.; Fadel, P.J.; Ogoh, S. Arterial baroreflex resetting during exercise: A current perspective. Exp. Physiol. 2006, 91, 37–49. [Google Scholar] [CrossRef] Viamontes, G.I.; Nemeroff, C.B. Brain-Body Interactions: The Physiological Impact of Mental Processes—The Neurobiology of the Stress Response. Psychiatr. Ann. 2009, 39, 975–984. [Google Scholar] [CrossRef] Bellone, E.; Hughes, J.; Guttorp, P.A. Hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts. Clim. Res. 2000, 15, 1–12. [Google Scholar] [CrossRef] Enke, W.; Spekat, A. Downscaling climate model outputs into local and regional weather elements by classification and regression. Clim. Res. 1997, 8, 195–207. [Google Scholar] [CrossRef] Gardner, M.; Dorling, S. Artificial neural networks (the multilayer perceptron)—A review of applications in the atmospheric sciences. Atmos. Environ. 1998, 32, 2627–2636. [Google Scholar] [CrossRef] Ghil, M.; Yiou, P.; Hallegatte, S.; Malamud, B.D.; Naveau, P.; Soloviev, A.; Friederichs, P.; Keilis-Borok, V.; Kondrashov, D.; Kossobokov, V.; et al. Extreme events: Dynamics, statistics and prediction. Nonlinear Processes Geophys. 2011, 18, 295–350. [Google Scholar] [CrossRef] Kirk-Davidoff, D.B. On the diagnosis of climate sensitivity using observations of fluctuations. Atmos. Chem. Phys. 2009, 9, 813–822. [Google Scholar] [CrossRef][Green Version] Miller, N.L.; Duffy, P.B.; Cayan, D.R.; Hidalgo, H.; Jin, J.; Kanamaru, H.; O’Brien, T.; Schlegel, N.J.; Sloan, L.C.; Snyder, M.A.; et al. An Analysis of Simulated California Climate Using Multiple Dynamical and Statistical Techniques. California Climate Change Center Paper CEC-500-2009-017-F. 2009. Available online: https://escholarship.org/uc/item/9hh481gh (accessed on 3 May 2022). Cofino, A.S.; Cano, R.; Sordo, C.; Gutierrez, J.M. Bayesian networks for probabilistic weather prediction. In Proceedings of the European Conference on Artificial Intelligence (ECAI), Lyon, France, 21–26 July 2002; pp. 695–699. [Google Scholar] Tralongo, P.; Ferrau, F.; Borsellino Verderame, F.; Caruso, M.; Giuffrida Butera, A.; Gebbia, V. Cancer patient-centered home care: A new model for health care in oncology. Ther. Clin. Risk Manag. 2011, 7, 387–392. [Google Scholar] [CrossRef][Green Version] Izquierdo, R.; Meyer, S.; Starren, J.; Goland, R.; Teresi, J.; Shea, S.; Weinstock, R.S. Detection and remediation of medically urgent situations using telemedicine case management for older patients with diabetes mellitus. Ther. Clin. Risk Manag. 2007, 3, 485–489. [Google Scholar] Shalaby, N.; Shalaby, N. Study of ambulatory blood pressure in diabetic children: Prediction of early renal insult. Ther. Clin. Risk Manag. 2015, 11, 1531. [Google Scholar] [CrossRef] [PubMed][Green Version] Gandarillas, M.Á.; Goswami, N. Merging current health care trends: Innovative perspective in aging care. Clin. Interv. Aging 2018, 13, 2083. [Google Scholar] [CrossRef][Green Version]
dspace.entity.typePublication
relation.isAuthorOfPublication876b7d58-77b0-4c90-8148-d460c8fe53be
relation.isAuthorOfPublication.latestForDiscovery876b7d58-77b0-4c90-8148-d460c8fe53be

Download

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
MA Gandarillas & N Goswami (2022) traducido al español.pdf
Size:
438.13 KB
Format:
Adobe Portable Document Format

Collections