CBView

Merging data in metabolic diagnosis

Gabriel Correia Brito, Rui Fonseca-Pinto, Maria P. Guarino, Marlene Lajes, Nuno Vieira Lopes

Research output: Contribution to journalConference article

1 Citation (Scopus)
8 Downloads (Pure)

Abstract

The metabolic syndrome is a set of risk factors associated with increased cardiovascular risk. These changes to the standard metabolic functions are associated with increased blood insulin and with insulin resistance, which is the common feature of disease pathophysiology. Although with admissible genetic inheritance, the metabolic syndrome symptoms increase with age, sedentarism, weight gain, tobacco, and poor dietetic habits. Due to their characteristics, clinical manifestations of metabolic diseases are perceived by the patient at advanced stages of metabolic dysfunction, when the risk of an acute cardiovascular event is high. Early detection of disturbed glucose homeostatic mechanisms, by recording efferent responses to stimuli like meal ingestion, is, therefore, a methodology with diagnostic potential acting as a predictive measure of metabolic dysfunction. This work presents a novel software (CBView) that analyses the records of physiological responses mediated by the carotid bodies to provocation tests, obtained by a new medical device (CBMeter) aiming to the early tracking of changes in autonomic responses that control metabolism, and thus providing quantitative metrics to assess metabolic dysfunction.

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Merging
Insulin
Tobacco
Metabolism
Glucose
Blood

Keywords

  • Clinical Decision Support Systems
  • Clinical Software
  • Health Monitoring
  • Metabolic Diseases

Cite this

Brito, G. C., Fonseca-Pinto, R., Guarino, M. P., Lajes, M., & Lopes, N. V. (2018). CBView: Merging data in metabolic diagnosis. Procedia Computer Science, 138, 244-249. https://doi.org/10.1016/j.procs.2018.10.035
Brito, Gabriel Correia ; Fonseca-Pinto, Rui ; Guarino, Maria P. ; Lajes, Marlene ; Lopes, Nuno Vieira. / CBView : Merging data in metabolic diagnosis. In: Procedia Computer Science. 2018 ; Vol. 138. pp. 244-249.
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abstract = "The metabolic syndrome is a set of risk factors associated with increased cardiovascular risk. These changes to the standard metabolic functions are associated with increased blood insulin and with insulin resistance, which is the common feature of disease pathophysiology. Although with admissible genetic inheritance, the metabolic syndrome symptoms increase with age, sedentarism, weight gain, tobacco, and poor dietetic habits. Due to their characteristics, clinical manifestations of metabolic diseases are perceived by the patient at advanced stages of metabolic dysfunction, when the risk of an acute cardiovascular event is high. Early detection of disturbed glucose homeostatic mechanisms, by recording efferent responses to stimuli like meal ingestion, is, therefore, a methodology with diagnostic potential acting as a predictive measure of metabolic dysfunction. This work presents a novel software (CBView) that analyses the records of physiological responses mediated by the carotid bodies to provocation tests, obtained by a new medical device (CBMeter) aiming to the early tracking of changes in autonomic responses that control metabolism, and thus providing quantitative metrics to assess metabolic dysfunction.",
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Brito, GC, Fonseca-Pinto, R, Guarino, MP, Lajes, M & Lopes, NV 2018, 'CBView: Merging data in metabolic diagnosis', Procedia Computer Science, vol. 138, pp. 244-249. https://doi.org/10.1016/j.procs.2018.10.035

CBView : Merging data in metabolic diagnosis. / Brito, Gabriel Correia; Fonseca-Pinto, Rui; Guarino, Maria P.; Lajes, Marlene; Lopes, Nuno Vieira.

In: Procedia Computer Science, Vol. 138, 01.01.2018, p. 244-249.

Research output: Contribution to journalConference article

TY - JOUR

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T2 - Merging data in metabolic diagnosis

AU - Brito, Gabriel Correia

AU - Fonseca-Pinto, Rui

AU - Guarino, Maria P.

AU - Lajes, Marlene

AU - Lopes, Nuno Vieira

PY - 2018/1/1

Y1 - 2018/1/1

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AB - The metabolic syndrome is a set of risk factors associated with increased cardiovascular risk. These changes to the standard metabolic functions are associated with increased blood insulin and with insulin resistance, which is the common feature of disease pathophysiology. Although with admissible genetic inheritance, the metabolic syndrome symptoms increase with age, sedentarism, weight gain, tobacco, and poor dietetic habits. Due to their characteristics, clinical manifestations of metabolic diseases are perceived by the patient at advanced stages of metabolic dysfunction, when the risk of an acute cardiovascular event is high. Early detection of disturbed glucose homeostatic mechanisms, by recording efferent responses to stimuli like meal ingestion, is, therefore, a methodology with diagnostic potential acting as a predictive measure of metabolic dysfunction. This work presents a novel software (CBView) that analyses the records of physiological responses mediated by the carotid bodies to provocation tests, obtained by a new medical device (CBMeter) aiming to the early tracking of changes in autonomic responses that control metabolism, and thus providing quantitative metrics to assess metabolic dysfunction.

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KW - Clinical Software

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