Fibroblast growth factor 23, mineral metabolism and mortality among elderly men (Swedish MrOs)

Background: Fibroblast growth factor 23 (FGF23) is the earliest marker of disturbed mineral metabolism as renal function decreases. Its serum levels are associated with mortality in dialysis patients, persons with chronic kidney disease (CKD) and prevalent cardiovascular disease (CVD), and it is associated with atherosclerosis, endothelial dysfunction and left ventricular hypertrophy in the general population. The primary aim of this study is to examine the association between FGF23 and mortality, in relation to renal function in the community. A secondary aim is to examine the association between FGF23 and CVD related death. Methods: The population-based cohort of MrOS Sweden included 3014 men (age 69-81 years). At inclusion intact FGF23, intact parathyroid hormone (PTH), 25 hydroxyl vitamin D (25D), calcium and phosphate were measured. Mortality data were collected after an average of 4.5 years follow-up. 352 deaths occurred, 132 of CVD. Association between FGF23 and mortality was analyzed in quartiles of FGF23. Kaplan-Meier curves and Log-rank test were used to examine time to events. Cox proportional hazards regression was used to examine the association between FGF23, in quartiles and as a continuous variable, with mortality. The associations were also analyzed in the sub-cohort with estimated glomerular filtration rate (eGFR) above 60 ml/min/1.73 m(2). Results: There was no association between FGF23 and all-cause mortality, Hazard ratio (HR) 95% confidence interval (CI): 1.02 (0.89-1.17). For CVD death the HR (95% CI) was 1.26 (0.99 - 1.59)/(1-SD) increase in log(10) FGF23 after adjustment for eGFR, and other confounders. In the sub-cohort with eGFR > 60 ml/min/1.73 m(2) the HR (95% CI) for CVD death was 55% (13-111)/(1-SD) increase in log(10) FGF23. Conclusions: FGF23 is not associated with mortality of all-cause in elderly community living men, but there is a weak association with CVD death, even after adjustment for eGFR and the other confounders. The association with CVD death is noticeable only in the sub-cohort with preserved renal function

LASSIM-A network inference toolbox for genome-wide mechanistic modeling

Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naive Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.Funding Agencies|Swedish research council [VR 2015-03807, VR2016-07108]; center for Industrial Information Technology; Free State of Thuringia; European Regional Development Fund; Deutsche Forschungsgemeinschaft CRC/Transregio</p