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 χ2 -test (187.0 < Tχ2 = 226.4) and independent validation data (70.8 < Tχ2 = 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.

A unified framework for prediction of liver steatosis dynamics in response to different diet and drug interventions.

BACKGROUND & AIMS: Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disorder, characterized by the accumulation of excess fat in the liver, and is a driving factor for various severe liver diseases. These multi-factorial and multi-timescale changes are observed in different clinical studies, but these studies have not been integrated into a unified framework. In this study, we aim to present such a unified framework in the form of a dynamic mathematical model. METHODS: For model training and validation, we collected data for dietary or drug-induced interventions aimed at reducing or increasing liver fat. The model was formulated using ordinary differential equations (ODEs) and the mathematical analysis, model simulation, model formulation and the model parameter estimation were all performed in MATLAB. RESULTS: Our mathematical model describes accumulation of fat in the liver and predicts changes in lipid fluxes induced by both dietary and drug interventions. The model is validated using data from a wide range of drug and dietary intervention studies and can predict both short-term (days) and long-term (weeks) changes in liver fat. Importantly, the model computes the contribution of each individual lipid flux to the total liver fat dynamics. Furthermore, the model can be combined with an established bodyweight model, to simulate even longer scenarios (years), also including the effects of insulin resistance and body weight. To help prepare for corresponding eHealth applications, we also present a way to visualize the simulated changes, using dynamically changing lipid droplets, seen in images of liver biopsies. CONCLUSION: In conclusion, we believe that the minimal model presented herein might be a useful tool for future applications, and to further integrate and understand data regarding changes in dietary and drug induced changes in ectopic TAG in the liver. With further development and validation, the minimal model could be used as a disease progression model for steatosis.

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

BACKGROUND: The 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 the 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 mathematical models. We have previously developed a model for short-term glucose homeostasis and intracellular insulin signaling, and there exist long-term weight regulation models. Herein, we combine these models into a first interconnected digital twin for the progression of insulin resistance in humans. METHODS: The model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox. RESULTS: The interconnected twin correctly predicts independent data from a weight increase study, both for weight-changes, fasting plasma insulin and glucose levels, and intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study with the weight loss drug topiramate. The model can also predict non-measured variables. CONCLUSIONS: The model presented herein constitutes the basis for a new digital twin technology, which in the future could be used to aid medical pedagogy and increase motivation and compliance and thus aid in the prevention and treatment of insulin resistance.

A rapid, non-invasive, clinical surveillance for CachExia, sarcopenia, portal hypertension, and hepatocellular carcinoma in end-stage liver disease: the ACCESS-ESLD study protocol.

BACKGROUND: Liver cirrhosis, the advanced stage of many chronic liver diseases, is associated with escalated risks of liver-related complications like decompensation and hepatocellular carcinoma (HCC). Morbidity and mortality in cirrhosis patients are linked to portal hypertension, sarcopenia, and hepatocellular carcinoma. Although conventional cirrhosis management centered on treating complications, contemporary approaches prioritize preemptive measures. This study aims to formulate novel blood- and imaging-centric methodologies for monitoring liver cirrhosis patients. METHODS: In this prospective study, 150 liver cirrhosis patients will be enrolled from three Swedish liver clinics. Their conditions will be assessed through extensive blood-based markers and magnetic resonance imaging (MRI). The MRI protocol encompasses body composition profile with Muscle Assement Score, portal flow assessment, magnet resonance elastography, and a abbreviated MRI for HCC screening. Evaluation of lifestyle, muscular strength, physical performance, body composition, and quality of life will be conducted. Additionally, DNA, serum, and plasma biobanking will facilitate future investigations. DISCUSSION: The anticipated outcomes involve the identification and validation of non-invasive blood- and imaging-oriented biomarkers, enhancing the care paradigm for liver cirrhosis patients. Notably, the temporal evolution of these biomarkers will be crucial for understanding dynamic changes. TRIAL REGISTRATION: Clinicaltrials.gov, registration identifier NCT05502198. Registered on 16 August 2022. Link: https://classic. CLINICALTRIALS: gov/ct2/show/NCT05502198 .

Mathematical models for biomarker calculation of drug-induced liver injury in humans and experimental models based on gadoxetate enhanced magnetic resonance imaging.

BACKGROUND: Drug induced liver injury (DILI) is a major concern when developing new drugs. A promising biomarker for DILI is the hepatic uptake rate of the contrast agent gadoxetate. This rate can be estimated using a novel approach combining magnetic resonance imaging and mathematical modeling. However, previous work has used different mathematical models to describe liver function in humans or rats, and no comparative study has assessed which model is most optimal to use, or focused on possible translatability between the two species. AIMS: Our aim was therefore to do a comparison and assessment of models for DILI biomarker assessment, and to develop a conceptual basis for a translational framework between the species. METHODS AND RESULTS: We first established which of the available pharmacokinetic models to use by identifying the most simple and identifiable model that can describe data from both human and rats. We then developed an extension of this model for how to estimate the effects of a hepatotoxic drug in rats. Finally, we illustrated how such a framework could be useful for drug dosage selection, and how it potentially can be applied in personalized treatments designed to avoid DILI. CONCLUSION: Our analysis provides clear guidelines of which mathematical model to use for model-based assessment of biomarkers for liver function, and it also suggests a hypothetical path to a translational framework for DILI.

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 study. Using this model, we can predict non-measured variables, e.g. hepatic glycogen and gluconeogenesis, in response to fasting and different diets. Furthermore, we exemplify how such metabolic responses can be successfully adapted to a specific individual's sex, weight, height, as well as to the individual's historical data on metabolite dynamics. This tool enables an offline digital twin technology.

Non-invasive diagnosis and staging of non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is considered to be the hepatic manifestation of the metabolic syndrome and is characterized by ectopic accumulation of triglycerides in the cytoplasm of hepatocytes, i.e., steatosis. NAFLD has become the most common chronic liver disease, with an estimated global prevalence of 25%. Although the majority of NAFLD patients will never experience liver-related complications, the progressive potential of NAFLD is indisputable, with 5-10% of subjects progressing to cirrhosis, end-stage liver disease, or hepatocellular carcinoma. NAFLD patients with advanced fibrosis are at the highest risk of developing cardiovascular and cirrhosis-related complications. Liver biopsy has hitherto been considered the reference method for evaluation of hepatic steatosis and fibrosis stage. Given the limitations of biopsy for widescale screening, non-invasive tests (NITs) for assessment of steatosis and fibrosis stage, including serum-based algorithms and ultrasound- and magnetic resonance-based methods, will play an increasing role in the management of NAFLD patients. This comprehensive review presents the advantages and limitations of NITs for identification of steatosis and advanced fibrosis in NAFLD. The clinical implications of using NITs to identify and manage NAFLD patients are also discussed.

A systems biology analysis of adrenergically stimulated adiponectin exocytosis in white adipocytes.

Circulating levels of the adipocyte hormone adiponectin are typically reduced in obesity, and this deficiency has been linked to metabolic diseases. It is thus important to understand the mechanisms controlling adiponectin exocytosis. This understanding is hindered by the high complexity of both the available data and the underlying signaling network. To deal with this complexity, we have previously investigated how different intracellular concentrations of Ca2+, cAMP, and ATP affect adiponectin exocytosis, using both patch-clamp recordings and systems biology mathematical modeling. Recent work has shown that adiponectin exocytosis is physiologically triggered via signaling pathways involving adrenergic ß3 receptors (ß3ARs). Therefore, we developed a mathematical model that also includes adiponectin exocytosis stimulated by extracellular epinephrine or the ß3AR agonist CL 316243. Our new model is consistent with all previous patch-clamp data as well as new data (collected from stimulations with a combination of the intracellular mediators and extracellular adrenergic stimuli) and can predict independent validation data. We used this model to perform new in silico experiments where corresponding wet lab experiments would be difficult to perform. We simulated adiponectin exocytosis in single cells in response to the reduction of ß3ARs that is observed in adipocytes from animals with obesity-induced diabetes. Finally, we used our model to investigate intracellular dynamics and to predict both cAMP levels and adiponectin release by scaling the model from single-cell to a population of cells-predictions corroborated by experimental data. Our work brings us one step closer to understanding the intricate regulation of adiponectin exocytosis.

Mechanisms of a Sustained Anti-inflammatory Drug Response in Alveolar Macrophages Unraveled with Mathematical Modeling.

Both initiation and suppression of inflammation are hallmarks of the immune response. If not balanced, the inflammation may cause extensive tissue damage, which is associated with common diseases, e.g., asthma and atherosclerosis. Anti-inflammatory drugs come with side effects that may be aggravated by high and fluctuating drug concentrations. To remedy this, an anti-inflammatory drug should have an appropriate pharmacokinetic half-life or better still, a sustained anti-inflammatory drug response. However, we still lack a quantitative mechanistic understanding of such sustained effects. Here, we study the anti-inflammatory response to a common glucocorticoid drug, dexamethasone. We find a sustained response 22 hours after drug removal. With hypothesis testing using mathematical modeling, we unravel the underlying mechanism-a slow release of dexamethasone from the receptor-drug complex. The developed model is in agreement with time-resolved training and testing data and is used to simulate hypothetical treatment schemes. This work opens up for a more knowledge-driven drug development to find sustained anti-inflammatory responses and fewer side effects.