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Non-invasive detection of divergent metabolic signals in insulin deficiency vs. insulin resistance in vivo.

ABSTRACT: The type 2 diabetic phenotype results from mixed effects of insulin deficiency and insulin resistance, but the relative contributions of these two distinct factors remain poorly characterized, as do the respective roles of the gluconeogenic organs. The purpose of this study was to investigate localized in vivo metabolic changes in liver and kidneys of contrasting models of diabetes mellitus (DM): streptozotocin (STZ)-treated wild-type Zucker rats (T1DM) and Zucker diabetic fatty (ZDF) rats (T2DM). Intermediary metabolism was probed using hyperpolarized (HP) [1-13C]pyruvate MRI of the liver and kidneys. These data were correlated with gene expression data for key mediators, assessed using rtPCR. Increased HP [1-13C]lactate was detected in both models, in association with elevated gluconeogenesis as reflected by increased expression of phosphoenolpyruvate carboxykinase. In contrast, HP [1-13C]alanine diverged between the two models, increasing in ZDF rats, while decreasing in the STZ-treated rats. The differences in liver alanine paralleled differences in key lipogenic mediators. Thus, HP [1-13C]alanine is a marker that can identify phenotypic differences in kidneys and liver of rats with T1DM vs. T2DM, non-invasively in vivo. This approach could provide a powerful diagnostic tool for characterizing tissue metabolic defects and responses to treatment in diabetic patients with ambiguous systemic manifestations.

SUBMITTER: Morze CV

PROVIDER: S-EPMC5794967 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionCardiovascular disease remains a significant problem in modern society. Among non-invasive techniques, the electrocardiogram (ECG) is one of the most reliable methods for detecting cardiac abnormalities. However, ECG interpretation requires expert knowledge and it is time-consuming. Developing a novel method to detect the disease early improves the quality and efficiency of medical care.MethodsThe paper presents various modern approaches for classifying cardiac diseases from ECG recordings. The first approach suggests the Poincaré representation of ECG signal and deep-learning-based image classifiers. Additionally, the raw signals were processed with the one-dimensional convolutional model while the XGBoost model was facilitated to predict based on the time-series features.ResultsThe Poincaré-based methods showed decent performance in predicting AF (atrial fibrillation) but not other types of arrhythmia. XGBoost model gave an acceptable performance in long-term data but had a long inference time due to highly-consuming calculations within the pre-processing phase. Finally, the 1D convolutional model, specifically the 1D ResNet, showed the best results in both studied CinC 2017 and CinC 2020 datasets, reaching the F1 score of 85% and 71%, respectively, and they were superior to the first-ranking solution of each challenge. The 1D models also presented high specificity. Additionally, our paper investigated efficiency metrics including power consumption and equivalent CO2 emissions, with one-dimensional models like 1D CNN and 1D ResNet being the most energy efficient. Model interpretation analysis showed that the DenseNet detected AF using heart rate variability while the 1D ResNet assessed the AF patterns in raw ECG signals.DiscussionDespite the under-performed results, the Poincaré diagrams are still worth studying further because of the accessibility and inexpensive procedure. In the 1D convolutional models, the residual connections are useful to keep the model simple but not decrease the performance. Our approach in power measurement and model interpretation helped understand the numerical complexity and mechanism behind the model decision.

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Non-invasive detection of divergent metabolic signals in insulin deficiency vs. insulin resistance in vivo.

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