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CBmeter 研究:评估外周化学感受器过度兴奋对代谢性疾病预测价值的方案。

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

机构信息

ciTechCare - Center for Innovative Care and Health Technology, Polytechnic Institute of Leiria, Leiria, Portugal.

Polytechnic of Castelo Branco School of Health, Castelo Branco, Portugal.

出版信息

BMJ Open. 2021 Aug 26;11(8):e042825. doi: 10.1136/bmjopen-2020-042825.

Abstract

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.

摘要

简介

早期筛查代谢疾病至关重要,因为持续未确诊的疾病会给医疗系统带来越来越大的负担。最近的研究表明,过度活跃的颈动脉体(CB)与 2 型糖尿病的发生之间存在关联。通过测量通气、心脏和代谢对挑战测试的反应来非侵入性评估 CB 活性可能对代谢疾病具有预测价值;然而,目前尚无评估 CB 活性的商业设备。CBmeter 研究的结果将阐明 CB 在代谢疾病发生中的作用,并指导开发用于代谢紊乱早期干预的新治疗方法。研究结果还可能有助于为未来的 CB 调节干预进行患者分类和分层。

方法

这是一项非随机、多中心、对照临床研究。将从二级和一级医疗保健机构招募 40 名参与者(20 名对照和 20 名糖尿病患者)。主要目的是基于对短暂 100%氧气吸入和标准混合餐摄入的呼吸和代谢反应,建立代谢疾病早期诊断的新模型。

分析

使用 CBmeter 获得的原始数据将与心率、呼吸率、氧饱和度和间质葡萄糖定量的金标准技术相对比,并使用专门为 CBmeter 研究开发的多变量分析软件(CBview)进行分析。将使用聚类分析和基于无监督学习算法的人工智能方法对数据进行分析,以确定糖尿病诊断的预测价值。

伦理

该研究已获得莱里亚医院中心伦理委员会的批准。将要求患者书面知情同意,并且数据将进行编码以确保数据的匿名性。

传播

研究结果将通过在同行评议期刊和相关医学和健康会议上发表来传播。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33ef/8395299/2bee0c046474/bmjopen-2020-042825f01.jpg

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