Dataset Information

Glucose prediction by analysis of exhaled metabolites - a systematic review.

ABSTRACT:

Background

In critically ill patients, glucose control with insulin mandates time- and blood-consuming glucose monitoring. Blood glucose level fluctuations are accompanied by metabolomic changes that alter the composition of volatile organic compounds (VOC), which are detectable in exhaled breath. This review systematically summarizes the available data on the ability of changes in VOC composition to predict blood glucose levels and changes in blood glucose levels.

Methods

A systematic search was performed in PubMed. Studies were included when an association between blood glucose levels and VOCs in exhaled air was investigated, using a technique that allows for separation, quantification and identification of individual VOCs. Only studies on humans were included.

Results

Nine studies were included out of 1041 identified in the search. Authors of seven studies observed a significant correlation between blood glucose levels and selected VOCs in exhaled air. Authors of two studies did not observe a strong correlation. Blood glucose levels were associated with the following VOCs: ketone bodies (e.g., acetone), VOCs produced by gut flora (e.g., ethanol, methanol, and propane), exogenous compounds (e.g., ethyl benzene, o-xylene, and m/p-xylene) and markers of oxidative stress (e.g., methyl nitrate, 2-pentyl nitrate, and CO).

Conclusion

There is a relation between blood glucose levels and VOC composition in exhaled air. These results warrant clinical validation of exhaled breath analysis to monitor blood glucose levels.

SUBMITTER: Leopold JH

PROVIDER: S-EPMC4068184 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Glucose prediction by analysis of exhaled metabolites - a systematic review.

Leopold Jan Hendrik JH van Hooijdonk Roosmarijn T M RT Sterk Peter J PJ Abu-Hanna Ameen A Schultz Marcus J MJ Bos Lieuwe D J LD

BMC anesthesiology 20140617

<h4>Background</h4>In critically ill patients, glucose control with insulin mandates time- and blood-consuming glucose monitoring. Blood glucose level fluctuations are accompanied by metabolomic changes that alter the composition of volatile organic compounds (VOC), which are detectable in exhaled breath. This review systematically summarizes the available data on the ability of changes in VOC composition to predict blood glucose levels and changes in blood glucose levels.<h4>Methods</h4>A syste ...[more]

PMID: 24963286

Similar Datasets

Project description:BackgroundMachine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important.ObjectiveIn a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity.MethodsPubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events.ResultsIn total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia.ConclusionsStatistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced.Trial registrationPROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

Dataset Information

Glucose prediction by analysis of exhaled metabolites - a systematic review.

Background

Methods

Results

Conclusion

Publications

Glucose prediction by analysis of exhaled metabolites - a systematic review.

Similar Datasets

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