Assessment of Comorbidity in Bariatric Patients through a Biomarker-Based Model-A Multicenter Validation of the Metabolic Health Index.

BACKGROUND: The metabolic health index (MHI) is a biomarker-based model that objectively assesses the cumulative impact of comorbidities type 2 diabetes mellitus, hypertension and dyslipidemia on the health state of bariatric patients. The MHI was developed on a single-center cohort using a fully laboratory data-driven approach, resulting in a MHI score on a range from 1 to 6. To show universal applicability in clinical care, the MHI was validated externally and potential laboratory-related shortcomings were evaluated. METHODS: Retrospective laboratory and national bariatric quality registry data were collected from five Dutch renowned bariatric centers (n = 11 501). MHI imprecision was derived from the cumulative effect of biological and analytical variance of the individual input variables of the MHI model. The performance of the MHI (model) was assessed in terms of discrimination and calibration. RESULTS: The cumulative imprecision in MHI was 0.25 MHI points. Calibration of the MHI model diverged over the different centers but was accounted for by misregistration of comorbidity after cross-checking the data. Discriminative performance of the MHI model was consistent across the different centers. CONCLUSIONS: The MHI model can be applied in clinical practice of bariatric centers, regardless of patient mix and analytical platform. Because the MHI is based on objective parameters, it is insensitive to diverging clinical definitions of comorbidities. Therefore, the MHI can be used to objectify severity of metabolic comorbidities in bariatric patients. The MHI can support the patient-selection process for surgery and objectively assessing the effect of surgery on the metabolic health state.

Simulating Metabolic Flexibility in Low Energy Expenditure Conditions Using Genome-Scale Metabolic Models.

Metabolic flexibility is the ability of an organism to adapt its energy source based on nutrient availability and energy requirements. In humans, this ability has been linked to cardio-metabolic health and healthy aging. Genome-scale metabolic models have been employed to simulate metabolic flexibility by computing the Respiratory Quotient (RQ), which is defined as the ratio of carbon dioxide produced to oxygen consumed, and varies between values of 0.7 for pure fat metabolism and 1.0 for pure carbohydrate metabolism. While the nutritional determinants of metabolic flexibility are known, the role of low energy expenditure and sedentary behavior in the development of metabolic inflexibility is less studied. In this study, we present a new description of metabolic flexibility in genome-scale metabolic models which accounts for energy expenditure, and we study the interactions between physical activity and nutrition in a set of patient-derived models of skeletal muscle metabolism in older adults. The simulations show that fuel choice is sensitive to ATP consumption rate in all models tested. The ability to adapt fuel utilization to energy demands is an intrinsic property of the metabolic network.

Dietary nitrate does not reduce oxygen cost of exercise or improve muscle mitochondrial function in patients with mitochondrial myopathy.

Muscle weakness and exercise intolerance negatively affect the quality of life of patients with mitochondrial myopathy. Short-term dietary nitrate supplementation has been shown to improve exercise performance and reduce oxygen cost of exercise in healthy humans and trained athletes. We investigated whether 1 wk of dietary inorganic nitrate supplementation decreases the oxygen cost of exercise and improves mitochondrial function in patients with mitochondrial myopathy. Ten patients with mitochondrial myopathy (40 ± 5 yr, maximal whole body oxygen uptake = 21.2 ± 3.2 ml·min-1·kg body wt-1, maximal work load = 122 ± 26 W) received 8.5 mg·kg body wt-1·day-1 inorganic nitrate (~7 mmol) for 8 days. Whole body oxygen consumption at 50% of the maximal work load, in vivo skeletal muscle oxidative capacity (evaluated from postexercise phosphocreatine recovery using 31P-magnetic resonance spectroscopy), and ex vivo mitochondrial oxidative capacity in permeabilized skinned muscle fibers (measured with high-resolution respirometry) were determined before and after nitrate supplementation. Despite a sixfold increase in plasma nitrate levels, nitrate supplementation did not affect whole body oxygen cost during submaximal exercise. Additionally, no beneficial effects of nitrate were found on in vivo or ex vivo muscle mitochondrial oxidative capacity. This is the first time that the therapeutic potential of dietary nitrate for patients with mitochondrial myopathy was evaluated. We conclude that 1 wk of dietary nitrate supplementation does not reduce oxygen cost of exercise or improve mitochondrial function in the group of patients tested.

Parameter trajectory analysis to identify treatment effects of pharmacological interventions.

The field of medical systems biology aims to advance understanding of molecular mechanisms that drive disease progression and to translate this knowledge into therapies to effectively treat diseases. A challenging task is the investigation of long-term effects of a (pharmacological) treatment, to establish its applicability and to identify potential side effects. We present a new modeling approach, called Analysis of Dynamic Adaptations in Parameter Trajectories (ADAPT), to analyze the long-term effects of a pharmacological intervention. A concept of time-dependent evolution of model parameters is introduced to study the dynamics of molecular adaptations. The progression of these adaptations is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages of the treatment. The trajectories provide insight in the affected underlying biological systems and identify the molecular events that should be studied in more detail to unravel the mechanistic basis of treatment outcome. Modulating effects caused by interactions with the proteome and transcriptome levels, which are often less well understood, can be captured by the time-dependent descriptions of the parameters. ADAPT was employed to identify metabolic adaptations induced upon pharmacological activation of the liver X receptor (LXR), a potential drug target to treat or prevent atherosclerosis. The trajectories were investigated to study the cascade of adaptations. This provided a counter-intuitive insight concerning the function of scavenger receptor class B1 (SR-B1), a receptor that facilitates the hepatic uptake of cholesterol. Although activation of LXR promotes cholesterol efflux and -excretion, our computational analysis showed that the hepatic capacity to clear cholesterol was reduced upon prolonged treatment. This prediction was confirmed experimentally by immunoblotting measurements of SR-B1 in hepatic membranes. Next to the identification of potential unwanted side effects, we demonstrate how ADAPT can be used to design new target interventions to prevent these.

On the identifiability of pharmacokinetic parameters in dynamic contrast-enhanced imaging.

The so-called "Kety model" is a two-compartment pharmacokinetic model describing tumor perfusion kinetics. Its parameters, the transendothelial transfer constant (K(trans)), extravascular extracellular volume fraction (upsilon(e)), and microvascular plasma volume fraction (upsilon(p)), can be estimated with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). However, the results obtained by current methods show large variation in predictability and reliability. Here, the aim was to examine which experimental conditions have to be fulfilled to avoid large uncertainties and mutual dependencies of the parameters. Using frequency response analysis and simulation, the identifiability of the model was examined. The requirements and influence of contrast enhancement measurements, such as temporal resolution, signal to noise ratio, and contrast injection rate, on the accuracy of the parameters were analyzed. Tissue response characteristics revealed a low-frequency system with a cutoff frequency equal to K(trans)/upsilon(e), which confines the required temporal resolution. For malignant tissue with hyperpermeable vasculature (high K(trans)) a higher sampling frequency is required to accurately estimate K(trans) than for normal tissue. Too low sampling rates or too low injection rates resulted in inaccurate K(trans) values and hereby unreliable classification of malignant tissue.

Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments.

Systems biology applies quantitative, mechanistic modelling to study genetic networks, signal transduction pathways and metabolic networks. Mathematical models of biochemical networks can look very different. An important reason is that the purpose and application of a model are essential for the selection of the best mathematical framework. Fundamental aspects of selecting an appropriate modelling framework and a strategy for model building are discussed. Concepts and methods from system and control theory provide a sound basis for the further development of improved and dedicated computational tools for systems biology. Identification of the network components and rate constants that are most critical to the output behaviour of the system is one of the major problems raised in systems biology. Current approaches and methods of parameter sensitivity analysis and parameter estimation are reviewed. It is shown how these methods can be applied in the design of model-based experiments which iteratively yield models that are decreasingly wrong and increasingly gain predictive power.