Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD)-A Condition Associated with Heightened Sympathetic Activation.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is the most common liver disease affecting a quarter of the global population and is often associated with adverse health outcomes. The increasing prevalence of MAFLD occurs in parallel to that of metabolic syndrome (MetS), which in fact plays a major role in driving the perturbations of cardiometabolic homeostasis. However, the mechanisms underpinning the pathogenesis of MAFLD are incompletely understood. Compelling evidence from animal and human studies suggest that heightened activation of the sympathetic nervous system is a key contributor to the development of MAFLD. Indeed, common treatment strategies for metabolic diseases such as diet and exercise to induce weight loss have been shown to exert their beneficial effects at least in part through the associated sympathetic inhibition. Furthermore, pharmacological and device-based approaches to reduce sympathetic activation have been demonstrated to improve the metabolic alterations frequently present in patients with obesity, MetSand diabetes. Currently available evidence, while still limited, suggests that sympathetic activation is of specific relevance in the pathogenesis of MAFLD and consequentially may offer an attractive therapeutic target to attenuate the adverse outcomes associated with MAFLD.

Machine learning powered tools for automated analysis of muscle sympathetic nerve activity recordings.

Automated analysis and quantification of physiological signals in clinical practice and medical research can reduce manual labor, increase efficiency, and provide more objective, reproducible results. To build a novel platform for the analysis of muscle sympathetic nerve activity (MSNA), we employed state-of-the-art data processing and machine learning applications. Data processing methods for integrated MSNA recordings were developed to evaluate signals regarding the overall quality of the signal, the validity of individual signal peaks regarding the potential to be MSNA bursts and the timing of their occurrence. An overall probability score was derived from this flexible platform to evaluate each individual signal peak automatically. Overall, three deep neural networks were designed and trained to validate individual signal peaks randomly sampled from recordings representing only electrical noise and valid microneurography recordings. A novel data processing method for the whole signal was developed to differentiate between periods of valid MSNA signal recordings and periods in which the signal was not available or lost due to involuntary movement of the recording electrode. A probabilistic model for timing of the signal bursts was implemented as part of the system. Machine Learning algorithms and data processing tools were implemented to replicate the complex decision-making process of manual MSNA analysis. Validation of manual MSNA analysis including intra- and inter-rater validity and a comparison with automated MSNA tools is required. The developed toolbox for automated MSNA analysis can be extended in a flexible way to include algorithms based on other datasets.