Donor complication rates in whole blood, plasma and platelet donors: Age versus experience.

BACKGROUND: Many blood banks use upper age limits for donors out of concern for a higher donor complication rate in older donors. Experienced donors are known to have lower donor complication rates, and older donors are often more experienced, confounding the effect of age on donor complication rate. STUDY DESIGN AND METHODS: We studied donor complication rates in whole blood, plasma, and plateletpheresis donors from 2012 to 2022. Donor complication rates were compared between age groups in inexperienced (<20th donation) and experienced (≥20th donation) donors. In addition to this direct comparison, we made use of logistic regression with finer-grained experience groups, to further quantify the effects of age, experience and other factors on donor complication rate. RESULTS: While overall rate of vasovagal reaction was lower, rate of moderate/severe vasovagal syncope was highest in 70-79 year donors, however, only reached significance for plasma donors. Furthermore, rates of failed stab were highest in this age group. Hematoma rate showed a U-shaped pattern with regard to age, where the rate was not higher in the 70-79 year age group than in the 18-23 year age group. Pain decreased with age, however, rates were higher in the 70-79 year age group than in the 65-69 year age group. DISCUSSION: When properly accounting for donor experience, donor complication rate profiles clearly change with age. The increased risk for moderate/severe vasovagal syncope in older donors should be clearly communicated. Extra caution is needed if these donors are accepted for first-time donations.

A Systems Analysis of Phenotype Heterogeneity in APOE*3Leiden.CETP Mice Induced by Long-Term High-Fat High-Cholesterol Diet Feeding.

Within the human population, considerable variability exists between individuals in their susceptibility to develop obesity and dyslipidemia. In humans, this is thought to be caused by both genetic and environmental variation. APOE*3-Leiden.CETP mice, as part of an inbred mouse model in which mice develop the metabolic syndrome upon being fed a high-fat high-cholesterol diet, show large inter-individual variation in the parameters of the metabolic syndrome, despite a lack of genetic and environmental variation. In the present study, we set out to resolve what mechanisms could underlie this variation. We used measurements of glucose and lipid metabolism from a six-month longitudinal study on the development of the metabolic syndrome. Mice were classified as mice with either high plasma triglyceride (responders) or low plasma triglyceride (non-responders) at the baseline. Subsequently, we fitted the data to a dynamic computational model of whole-body glucose and lipid metabolism (MINGLeD) by making use of a hybrid modelling method called Adaptations in Parameter Trajectories (ADAPT). ADAPT integrates longitudinal data, and predicts how the parameters of the model must change through time in order to comply with the data and model constraints. To explain the phenotypic variation in plasma triglycerides, the ADAPT analysis suggested a decreased cholesterol absorption, higher energy expenditure and increased fecal fatty acid excretion in non-responders. While decreased cholesterol absorption and higher energy expenditure could not be confirmed, the experimental validation demonstrated that the non-responders were indeed characterized by increased fecal fatty acid excretion. Furthermore, the amount of fatty acids excreted strongly correlated with bile acid excretion, in particular deoxycholate. Since bile acids play an important role in the solubilization of lipids in the intestine, these results suggest that variation in bile acid homeostasis may in part drive the phenotypic variation in the APOE*3-Leiden.CETP mice.

A computational model for prediction of ferritin and haemoglobin levels in blood donors.

Blood donors are at risk of iron deficiency anaemia. While this risk is decreased through ferritin-based deferral, ideally ferritin monitoring should also aid in optimising donation frequencies. We extended an existing model of haemoglobin (Hb) synthesis with iron homeostasis and validated the model on a cohort of 300 new donors whose ferritin levels were measured from stored blood samples collected over a 2-year period. We then used the donor's gender, body weight, height, and baseline Hb and ferritin levels to predict subsequent Hb and ferritin levels. The prediction error was within measurement variability in 88% of Hb level predictions and 64% of ferritin level predictions. A sensitivity analysis of the model revealed that baseline ferritin level was the most important in predicting future ferritin levels. Finally, we used the model to calculate the annual donation frequency at which donors would keep their ferritin level >15 ng/ml when measured after donating for 2 years. The mean annual donation frequency would then be 1.9 for women and 4.1 for men. The computational model, requiring baseline values only, can predict future Hb and ferritin levels remarkably well. This enables determination of optimal donation frequencies for individual donors at the start of their donation career.

A hierarchical dynamic model used for investigating feed efficiency and its relationship with hepatic gene expression in APOE*3-Leiden.CETP mice.

BACKGROUND: Feed efficiency (FE) is an important trait for livestock and humans. While the livestock industry focuses on increasing FE, in the current obesogenic society it is more of interest to decrease FE. Hence, understanding mechanisms involved in the regulation of FE and particularly how it can be decreased would help tremendously in counteracting the obesity pandemic. However, it is difficult to accurately measure or calculate FE in humans. In this study, we aimed to address this challenge by developing a hierarchical dynamic model based on humanized mouse data. METHODS: We analyzed existing experimental data derived from 105 APOE*3-Leiden.CETP (E3L.CETP) mice fed a high-fat high-cholesterol (HFHC) diet for 1 (N = 20), 2 (N = 19), 3 (N = 20), and 6 (N = 46) month. We developed an ordinary differential equation (ODE) based model to estimate the FE based on the longitudinal data of body weight and food intake. Since the liver plays an important role in maintaining metabolic homeostasis, we evaluated associations between FE and hepatic gene expression levels. Depending on the feeding duration, we observed different relationships between FE and hepatic gene expression levels. RESULTS: After 1-month feeding of HFHC diet, we observed that FE was associated with vitamin A metabolism, arachidonic acid metabolism, and the PPAR signaling pathway. After 3- and 6-month feeding of HFHC diet, we observed that FE was associated most strongly with expression levels of Spink1 and H19, genes involved in cell proliferation and glucose metabolism, respectively. CONCLUSIONS: In conclusion, our analysis suggests that various biological processes such as vitamin A metabolism, hepatic response to inflammation, and cell proliferation associate with FE at different stages of diet-induced obesity.

In vivo and in silico dynamics of the development of Metabolic Syndrome.

The Metabolic Syndrome (MetS) is a complex, multifactorial disorder that develops slowly over time presenting itself with large differences among MetS patients. We applied a systems biology approach to describe and predict the onset and progressive development of MetS, in a study that combined in vivo and in silico models. A new data-driven, physiological model (MINGLeD: Model INtegrating Glucose and Lipid Dynamics) was developed, describing glucose, lipid and cholesterol metabolism. Since classic kinetic models cannot describe slowly progressing disorders, a simulation method (ADAPT) was used to describe longitudinal dynamics and to predict metabolic concentrations and fluxes. This approach yielded a novel model that can describe long-term MetS development and progression. This model was integrated with longitudinal in vivo data that was obtained from male APOE*3-Leiden.CETP mice fed a high-fat, high-cholesterol diet for three months and that developed MetS as reflected by classical symptoms including obesity and glucose intolerance. Two distinct subgroups were identified: those who developed dyslipidemia, and those who did not. The combination of MINGLeD with ADAPT could correctly predict both phenotypes, without making any prior assumptions about changes in kinetic rates or metabolic regulation. Modeling and flux trajectory analysis revealed that differences in liver fluxes and dietary cholesterol absorption could explain this occurrence of the two different phenotypes. In individual mice with dyslipidemia dietary cholesterol absorption and hepatic turnover of metabolites, including lipid fluxes, were higher compared to those without dyslipidemia. Predicted differences were also observed in gene expression data, and consistent with the emergence of insulin resistance and hepatic steatosis, two well-known MetS co-morbidities. Whereas MINGLeD specifically models the metabolic derangements underlying MetS, the simulation method ADAPT is generic and can be applied to other diseases where dynamic modeling and longitudinal data are available.

Male apoE*3-Leiden.CETP mice on high-fat high-cholesterol diet exhibit a biphasic dyslipidemic response, mimicking the changes in plasma lipids observed through life in men.

Physiological adaptations resulting in the development of the metabolic syndrome in man occur over a time span of several decades. This combined with the prohibitive financial cost and ethical concerns to measure key metabolic parameters repeatedly in subjects for the major part of their life span makes that comprehensive longitudinal human data sets are virtually nonexistent. While experimental mice are often used, little is known whether this species is in fact an adequate model to better understand the mechanisms that drive the metabolic syndrome in man. We took up the challenge to study the response of male apoE*3-Leiden.CETP mice (with a humanized lipid profile) to a high-fat high-cholesterol diet for 6 months. Study parameters include body weight, food intake, plasma and liver lipids, hepatic transcriptome, VLDL - triglyceride production and importantly the use of stable isotopes to measure hepatic de novo lipogenesis, gluconeogenesis, and biliary/fecal sterol secretion to assess metabolic fluxes. The key observations include (1) high inter-individual variation; (2) a largely unaffected hepatic transcriptome at 2, 3, and 6 months; (3) a biphasic response curve of the main metabolic features over time; and (4) maximum insulin resistance preceding dyslipidemia. The biphasic response in plasma triglyceride and total cholesterol appears to mimic that of men in cross-sectional studies. Combined, these observations suggest that studies such as these can help to delineate the causes of metabolic derangements in patients suffering from metabolic syndrome.

Developments in intestinal cholesterol transport and triglyceride absorption.

PURPOSE OF REVIEW: To discuss recent advances in research focused on intestinal lipid handling. RECENT FINDINGS: An important strategy in reducing atherosclerosis and risk of cardiovascular events is to increase the rate of reverse cholesterol transport, including its final step; cholesterol excretion from the body. The rate of removal is determined by a complex interplay between the factors involved in regulation of intestinal cholesterol absorption. One of these factors is a process known as transintestinal cholesterol excretion. This pathway comprises transport of cholesterol directly from the blood, through the enterocyte, into the intestinal lumen. In humans, this pathway accounts for 35% of cholesterol excretion in the feces. Mechanistic studies in mice revealed that, activation of the bile acid receptor farnesoid X receptor increases cholesterol removal via the transintestinal cholesterol excretion pathway as well as decreases plasma cholesterol and triglyceride providing an interesting target for treatment of dyslipidemia in humans. The physical chemical properties of bile acids are under control of farnesoid X receptor and determine intestinal cholesterol and triglyceride solubilization as well as absorption, providing a direct link between these two important factors in the pathogenesis of cardiovascular disease. Besides bile acids, intestinal phospholipids are important for luminal lipid solubilization. Interestingly, phospholipid remodeling through LPCAT3 was shown to be pivotal for uptake of fatty acids by enterocytes, which may provide a mechanistic handle for therapeutic intervention. SUMMARY: The importance of the intestine in control of cholesterol and triglyceride homeostasis is increasingly recognized. Recently, novel factors involved in regulation of cholesterol excretion and intestinal triglyceride and fatty acid uptake have been reported and are discussed in this short review.

Transintestinal Cholesterol Transport Is Active in Mice and Humans and Controls Ezetimibe-Induced Fecal Neutral Sterol Excretion.

Except for conversion to bile salts, there is no major cholesterol degradation pathway in mammals. Efficient excretion from the body is therefore a crucial element in cholesterol homeostasis. Yet, the existence and importance of cholesterol degradation pathways in humans is a matter of debate. We quantified cholesterol fluxes in 15 male volunteers using a cholesterol balance approach. Ten participants repeated the protocol after 4 weeks of treatment with ezetimibe, an inhibitor of intestinal and biliary cholesterol absorption. Under basal conditions, about 65% of daily fecal neutral sterol excretion was bile derived, with the remainder being contributed by direct transintestinal cholesterol excretion (TICE). Surprisingly, ezetimibe induced a 4-fold increase in cholesterol elimination via TICE. Mouse studies revealed that most of ezetimibe-induced TICE flux is mediated by the cholesterol transporter Abcg5/Abcg8. In conclusion, TICE is active in humans and may serve as a novel target to stimulate cholesterol elimination in patients at risk for cardiovascular disease.

Evaluating computational models of cholesterol metabolism.

Regulation of cholesterol homeostasis has been studied extensively during the last decades. Many of the metabolic pathways involved have been discovered. Yet important gaps in our knowledge remain. For example, knowledge on intracellular cholesterol traffic and its relation to the regulation of cholesterol synthesis and plasma cholesterol levels is incomplete. One way of addressing the remaining questions is by making use of computational models. Here, we critically evaluate existing computational models of cholesterol metabolism making use of ordinary differential equations and addressed whether they used assumptions and make predictions in line with current knowledge on cholesterol homeostasis. Having studied the results described by the authors, we have also tested their models. This was done primarily by testing the effect of statin treatment in each model. Ten out of eleven models tested have made assumptions in line with current knowledge of cholesterol metabolism. Three out of the ten remaining models made correct predictions, i.e. predicting a decrease in plasma total and LDL cholesterol or increased uptake of LDL upon treatment upon the use of statins. In conclusion, few models on cholesterol metabolism are able to pass a functional test. Apparently most models have not undergone the critical iterative systems biology cycle of validation. We expect modeling of cholesterol metabolism to go through many more model topologies and iterative cycles and welcome the increased understanding of cholesterol metabolism these are likely to bring.

Pep2Path: automated mass spectrometry-guided genome mining of peptidic natural products.

Nonribosomally and ribosomally synthesized bioactive peptides constitute a source of molecules of great biomedical importance, including antibiotics such as penicillin, immunosuppressants such as cyclosporine, and cytostatics such as bleomycin. Recently, an innovative mass-spectrometry-based strategy, peptidogenomics, has been pioneered to effectively mine microbial strains for novel peptidic metabolites. Even though mass-spectrometric peptide detection can be performed quite fast, true high-throughput natural product discovery approaches have still been limited by the inability to rapidly match the identified tandem mass spectra to the gene clusters responsible for the biosynthesis of the corresponding compounds. With Pep2Path, we introduce a software package to fully automate the peptidogenomics approach through the rapid Bayesian probabilistic matching of mass spectra to their corresponding biosynthetic gene clusters. Detailed benchmarking of the method shows that the approach is powerful enough to correctly identify gene clusters even in data sets that consist of hundreds of genomes, which also makes it possible to match compounds from unsequenced organisms to closely related biosynthetic gene clusters in other genomes. Applying Pep2Path to a data set of compounds without known biosynthesis routes, we were able to identify candidate gene clusters for the biosynthesis of five important compounds. Notably, one of these clusters was detected in a genome from a different subphylum of Proteobacteria than that in which the molecule had first been identified. All in all, our approach paves the way towards high-throughput discovery of novel peptidic natural products. Pep2Path is freely available from http://pep2path.sourceforge.net/, implemented in Python, licensed under the GNU General Public License v3 and supported on MS Windows, Linux and Mac OS X.