13C-glucose breath tests: a non-invasive method for detecting early clinical manifestations of exogenous glucose metabolism in type 2 diabetic patients

Effect of Chronic Ethanol Consumption on Exogenous Glucose Metabolism in Rats Using [1-13C], [2-13C], and [3-13C]glucose Breath Tests

The C3 carbon of glucose molecules becomes the C1 carbon of pyruvate molecules during glycolysis, and the C1 and C2 carbons of glucose molecules are metabolized in the tricarboxylic acid (TCA) cycle. Utilizing this position-dependent metabolism of C atoms in glucose molecules, [1-13C], [2-13C], and [3-13C]glucose breath tests are used to evaluate glucose metabolism. However, the effects of chronic ethanol consumption remain incompletely understood. Therefore, we evaluated glucose metabolism in ethanol-fed rats using [1-13C], [2-13C], and [3-13C]glucose breath tests. Ethanol-fed (ERs) and control rats (CRs) (n = 8 each) were used in this study, and ERs were prepared by replacing drinking water with a 16% ethanol solution. We administered 100 mg/kg of [1-13C], [2-13C], or [3-13C]glucose to rats and collected expired air (at 10-min intervals for 180 min). We compared the 13CO2 levels (Δ13CO2, ‰) of breath measured by IR isotope ratio spectrometry and area under the curve (AUC) values of the 13CO2 levels-time curve between ERs and CRs. 13CO2 levels and AUCs after administration of [1-13C]glucose and [2-13C]glucose were lower in ERs than in CRs. Conversely, the AUC for the [3-13C]glucose breath test showed no significant differences between ERs and CRs, although 13CO2 levels during the 110-120 min interval were significantly high in ERs. These findings indicate that chronic ethanol consumption diminishes glucose oxidation without concomitantly reducing glycolysis. Our study demonstrates the utility of 13C-labeled glucose breath tests as noninvasive and repeatable methods for evaluating glucose metabolism in various subjects, including those with alcoholism or diabetes.

DiaNet v2 deep learning based method for diabetes diagnosis using retinal images

Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and accessible diagnostic methods are essential. Current diagnostic tests like fasting plasma glucose (FPG), oral glucose tolerance tests (OGTT), random plasma glucose (RPG), and hemoglobin A1c (HbA1c) have limitations, leading to misclassifications and discomfort for patients. The aim of this study is to enhance diabetes diagnosis accuracy by developing an improved predictive model using retinal images from the Qatari population, addressing the limitations of current diagnostic methods. This study explores an alternative approach involving retinal images, building upon the DiaNet model, the first deep learning model for diabetes detection based solely on retinal images. The newly proposed DiaNet v2 model is developed using a large dataset from Qatar Biobank (QBB) and Hamad Medical Corporation (HMC) covering wide range of pathologies in the the retinal images. Utilizing the most extensive collection of retinal images from the 5545 participants (2540 diabetic patients and 3005 control), DiaNet v2 is developed for diabetes diagnosis. DiaNet v2 achieves an impressive accuracy of over 92%, 93% sensitivity, and 91% specificity in distinguishing diabetic patients from the control group. Given the high prevalence of diabetes and the limitations of existing diagnostic methods in clinical setup, this study proposes an innovative solution. By leveraging a comprehensive retinal image dataset and applying advanced deep learning techniques, DiaNet v2 demonstrates a remarkable accuracy in diabetes diagnosis. This approach has the potential to revolutionize diabetes detection, providing a more accessible, non-invasive and accurate method for early intervention and treatment planning, particularly in regions with high diabetes rates like MENA.

Non-invasive blood glucose monitoring technology in diabetes management: review

Diabetes is one of the leading non-communicable diseases globally, adversely impacting an individual's quality of life and adding a considerable burden to the healthcare systems. The necessity for frequent blood glucose (BG) monitoring and the inconveniences associated with self-monitoring of BG, such as pain and discomfort, has motivated the development of non-invasive BG approaches. However, the current research progress is slow, and only a few BG self-monitoring devices have made considerable progress. Hence, we evaluate the available non-invasive glucose monitoring technologies validated against BG recordings to provide future research direction to design, develop, and deploy self-monitoring of BG with integrated emerging technologies. We searched five databases, Embase, MEDLINE, Proquest, Scopus, and Web of Science, to assess the non-invasive technology's scope in the diabetes management paradigm published from 2000 to 2020. A total of three approaches to non-invasive screening, including saliva, skin, and breath, were identified and discussed. We observed a statistical relationship between BG measurements obtained from non-invasive methods and standard clinical measures. Opportunities exist for future research to advance research progress and facilitate early technology adoption for healthcare practice. The results promise clinical validity; however, formulating regulatory guidelines could foresee the deployment of approved non-invasive BG monitoring technologies in healthcare practice. Further, research prospects are there to design, develop, and deploy integrated diabetes management systems with mobile technologies, data analytics, and the internet of things (IoT) to deliver a personalised monitoring system.

European guideline on indications, performance and clinical impact of 13 C-breath tests in adult and pediatric patients: An EAGEN, ESNM, and ESPGHAN consensus, supported by EPC

INTRODUCTION

13 C-breath tests are valuable, noninvasive diagnostic tests that can be widely applied for the assessment of gastroenterological symptoms and diseases. Currently, the potential of these tests is compromised by a lack of standardization regarding performance and interpretation among expert centers.

METHODS

This consensus-based clinical practice guideline defines the clinical indications, performance, and interpretation of 13 C-breath tests in adult and pediatric patients. A balance between scientific evidence and clinical experience was achieved by a Delphi consensus that involved 43 experts from 18 European countries. Consensus on individual statements and recommendations was established if ≥ 80% of reviewers agreed and <10% disagreed.

RESULTS

The guideline gives an overview over general methodology of 13 C-breath testing and provides recommendations for the use of 13 C-breath tests to diagnose Helicobacter pylori infection, measure gastric emptying time, and monitor pancreatic exocrine and liver function in adult and pediatric patients. Other potential applications of 13 C-breath testing are summarized briefly. The recommendations specifically detail when and how individual 13 C-breath tests should be performed including examples for well-established test protocols, patient preparation, and reporting of test results.

CONCLUSION

This clinical practice guideline should improve pan-European harmonization of diagnostic approaches to symptoms and disorders, which are very common in specialist and primary care gastroenterology practice, both in adult and pediatric patients. In addition, this guideline identifies areas of future clinical research involving the use of 13 C-breath tests.