CBmeter study: protocol for assessing the predictive value of peripheral chemoreceptor overactivation for metabolic diseases

article

Lages, Marlene
Carvalho, Lucinda
Feijó, Salvato
Vieira, Alexandra
Pinto, Rui Fonseca
Guarino, Maria Pedro

BMJ

LAGES, Marlene [et al.] (2021) - CBmeter study: protocol for assessing the predictive value of peripheral chemoreceptor overactivation for metabolic diseases. BMJ Open. Vol. 11, Nº 8. DOI: 10.1136/bmjopen-2020-042825

2044-6055

10.1136/bmjopen-2020-042825

CBmeter study: protocol for assessing the predictive value of peripheral chemoreceptor overactivation for metabolic diseases

Health informatics
Physiology
Protocols
Guidelines

2021-11-20T16:10:38Z
2021-11-20T16:10:38Z
2021

Introduction Early screening of metabolic diseases is crucial since continued undiagnostic places an ever-increasing burden on healthcare systems. Recent studies suggest a link between overactivated carotid bodies (CB) and the genesis of type 2 diabetes mellitus. The non-invasive assessment of CB activity by measuring ventilatory, cardiac and metabolic responses to challenge tests may have predictive value for metabolic diseases; however, there are no commercially available devices that assess CB activity. The findings of the CBmeter study will clarify the role of the CBs in the genesis of—metabolic diseases and guide the development of new therapeutic approaches for early intervention in metabolic disturbances. Results may also contribute to patient classification and stratification for future CB modulatory interventions. Methods This is a non-randomised, multicentric, controlled clinical study. Forty participants (20 control and 20 diabetics) will be recruited from secondary and primary healthcare settings. The primary objective is to establish a new model of early diagnosis of metabolic diseases based on the respiratory and metabolic responses to transient 100% oxygen administration and ingestion of a standardised mixed meal. Analysis Raw data acquired with the CBmeter will be endorsed against gold standard techniques for heart rate, respiratory rate, oxygen saturation and interstitial glucose quantification and analysed a multivariate analysis software developed specifically for the CBmeter study (CBview). Data will be analysed using clustering analysis and artificial intelligence methods based on unsupervised learning algorithms, to establish the predictive value of diabetes diagnosis. Ethics The study was approved by the Ethics Committee of the Leiria Hospital Centre. Patients will be asked for written informed consent and data will be coded to ensure the anonymity of data. Dissemination Results will be disseminated through publication in peer-reviewed journals and relevant medical and health conferences.
info:eu-repo/semantics/publishedVersion

openAccess
http://creativecommons.org/licenses/by/4.0/

eng