Biology subjects

Ekstedt, M.

Publications and source records attributed to Ekstedt, M..

4 recordsLinked to original sources

bioRxiv2023

A Physiologically Based Digital Twin for Alcohol Consumption - Predicting Real-life Drinking Responses and Long-term Plasma PEth

Alcohol consumption is associated with a wide variety of preventable health complications and is a major risk factor for all-cause mortality in the age group 15-47 years. To reduce dangerous drinking behavior, eHealth applications have shown promise. A particularly interesting potential lies in the combination of eHealth apps with mathematical models. However, existing mathematical models do not consider real-life situations, such as combined intake of meals and beverages, and do not connect drinking to clinical markers, such as phosphatidylethanol (PEth). Herein, we present such a model which can simulate real-life situations and connect drinking to long-term markers. The new model can accurately describe both estimation data according to a {chi}2-test (187.0 < T{chi}2 = 226.4) and independent validation data (70.8 < T{chi}2 =93.5). The model can also be personalized using anthropometric data from a specific individual and can thus be used as a physiologically-based digital twin. This twin is also able to connect short-term consumption of alcohol to the long-term dynamics of PEth levels in the blood, a clinical biomarker of alcohol consumption. Here we illustrate how connecting short-term consumption to long-term markers allows for a new way to determine patient alcohol consumption from measured PEth levels. An additional use case of the twin could include the combined evaluation of patient-reported AUDIT forms and measured PEth levels. Finally, we integrated the new model into an eHealth application, which could help guide individual users or clinicians to help reduce dangerous drinking.

systems biology↗

bioRxiv2023

A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects

AimsThe increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as development of type 2 diabetes. Insulin resistance also occurs on different physiological levels, ranging from disease phenotypes to organ-organ communication and intracellular signaling. To better understand the progression of insulin resistance, an analysis method is needed that can combine different timescales and physiological levels. One such method is digital twins, consisting of combined mechanistic multi-scale and multi-level mathematical models. We have previously developed a multi-level model for short-term glucose homeostasis and intracellular insulin signaling, and there exists long-term weight regulation models. However, no one has combined these kinds of models into an interconnected, multi-level and multi-timescale digital twin model. Herein, we present a first such multi-scale digital twin for the progression of insulin resistance in humans. MethodsThe model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox. ResultsThe connected twin correctly predicts independent data from a weight increase study, both for weight-changes, for fasting plasma insulin and glucose levels, as well as for intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study, involving diet and the weight loss drug topiramate. These independent validation tests are confirmed by a chi-square test (() = 4.8 < 21 = 2, (12,0.05)). In both these cases, the model can also predict non-measured variables, such as activity of intracellular intermediaries, glucose tolerance responses, and organ fluxes. ConclusionsWe present a first multi-level and multi-timescale model, describing dynamics on the whole-body, organ and cellular levels, ranging from minutes to years. This model constitutes the basis for a new digital twin technology, which in the future could potentially be used to aid medical pedagogics and increase motivation and compliance and thus aid in prevention and treatment of insulin resistance.

systems biology↗

bioRxiv2021

Digital twin predicting diet response before and after long-term fasting

Today, there is great interest in diets proposing new combinations of macronutrient compositions and fasting schedules. Unfortunately, there is little consensus regarding the impact of these different diets, since available studies measure different sets of variables in different populations, thus only providing partial, non-connected insights. We lack an approach for integrating all such partial insights into a useful and interconnected big picture. Herein, we present such an integrating tool. The tool uses a novel mathematical model that describes mechanisms regulating diet-response and fasting metabolic fluxes, both for organ-organ crosstalk, and inside the liver. The tool can mechanistically explain and integrate data from several clinical studies, and correctly predict new independent data, including data from a new clinical study. Using this model, we can predict non-measured variables, e.g. hepatic glycogen and gluconeogenesis, and we can quantify personalized expected differences in outcome for any diet. This constitutes a new digital twin technology.

systems biology↗

bioRxiv2020

Macrophage Scavenger Receptor 1 mediates lipid-induced inflammation in non-alcoholic fatty liver disease

Obesity-associated inflammation is a key player in the pathogenesis of non-alcoholic fatty liver disease (NAFLD). However, the exact mechanisms remain incompletely understood. Here we demonstrate that macrophage scavenger receptor 1 (MSR1, CD204) expression is associated with the occurrence of hepatic lipid-laden foamy macrophages and correlates with the degree of steatosis and steatohepatitis in a cohort of 170 NAFLD patients. Mice lacking Msr1 are protected against high fat-cholesterol diet (HFD)-induced metabolic disorder, showing fewer hepatic lipid-laden foamy macrophages, less hepatic inflammation, improved dyslipidemia and glucose tolerance, while showing a change in hepatic lipid metabolism. We show that MSR1 induces a pro-inflammatory response via the JNK signaling pathway upon triggering by saturated fatty acids. In vitro blockade of the receptor prevented the accumulation of lipids in primary macrophages which inhibited the switch towards a pro-inflammatory phenotype and the release of cytokines such as TNFa. Targeting MSR1 using monoclonal antibody therapy in an obesity-associated NAFLD mouse model and ex vivo human liver slices resulted in the prevention of foamy macrophage formation and liver inflammation. Moreover, we identified that rs41505344, a polymorphism in the upstream transcriptional region of MSR1, was associated with altered serum triglycerides and aspartate transaminase levels in a cohort of over 400,000 patients. Taken together, our data suggest a critical role for MSR1 in lipid homeostasis and a potential therapeutic target for the treatment of NAFLD. One Sentence SummaryThe immunometabolic role of MSR1 in human NAFLD.

immunology↗