Space-time interactions in Bayesian disease mapping with recent tools: Making things easier for practitioners

Spatio-temporal disease mapping studies the distribution of mortality or incidence risks in space and its evolution in time, and it usually relies on fitting hierarchical Poisson mixed models. These models are complex for practitioners as they generally require adding constraints to correctly identify and interpret the different model terms. However, including constraints may not be straightforward in some recent software packages. This paper focuses on NIMBLE, a library of algorithms that contains among others a configurable system for Markov chain Monte Carlo (MCMC) algorithms. In particular, we show how to fit different spatio-temporal disease mapping models with NIMBLE making emphasis on how to include sum-to-zero constraints to solve identifiability issues when including spatio-temporal interactions. Breast cancer mortality data in Spain during the period 1990-2010 is used for illustration purposes. A simulation study is also conducted to compare NIMBLE with R-INLA in terms of parameter estimates and relative risk estimation. The results are very similar but differences are observed in terms of computing time.

STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package.

Targeted lipidomics reveals extensive changes in circulating lipid mediators in patients with acutely decompensated cirrhosis

BACKGROUND & AIMS

Acute-on-chronic liver failure (ACLF) is a newly described syndrome, which develops in patients with acute decompensation of cirrhosis, and is characterized by intense systemic inflammation, multiple organ failures and high short-term mortality. The profile of circulating lipid mediators, which are endogenous signaling molecules that play a major role in inflammation and immunity, is poorly characterized in ACLF.

METHODS

In the current study, we assessed the profile of lipid mediators by liquid chromatography coupled to tandem mass spectrometry in plasma from patients with acute decompensation of cirrhosis, with (n = 119) and without (n = 127) ACLF, and from healthy controls (n = 18). Measurements were prospectively repeated in 191 patients with acute decompensation of cirrhosis during a 28-day follow-up period.

RESULTS

Fifty-nine lipid mediators (out of 100) were detected in plasma from cirrhotic patients, of which 16 were significantly associated with disease status. Among these, 11 lipid mediators distinguished patients at any stage from healthy controls, whereas 2 lipid mediators (LTE₄ and 12-HHT, both derived from arachidonic acid) shaped a minimal plasma fingerprint that discriminated patients with ACLF from those without. Levels of LTE₄ distinguished ACLF grade 3 from ACLF grades 1 and 2, followed the clinical course of the disease (increased with worsening and decreased with improvement) and positively correlated with markers of inflammation and non-apoptotic cell death. Moreover, LTE₄ together with LXA₅ (derived from eicosapentaenoic acid) and EKODE (derived from linoleic acid) were associated with short-term mortality. LXA₅ and EKODE formed a signature associated with coagulation and liver failures.

CONCLUSION

Taken together, these findings uncover specific lipid mediator profiles associated with disease severity and prognosis in patients with acute decompensation of cirrhosis.

LAY SUMMARY

Acute-on-chronic liver failure (ACLF) is characterized by intense systemic inflammation, multiple organ failures and high short-term mortality. In the current study, we assessed the plasma lipid profile of 100 bioactive lipid mediators in healthy controls, patients with decompensated cirrhosis, and those who had developed ACLF. We identified lipid mediator signatures associated with inflammation and non-apoptotic cell death that discriminate disease severity and evolution, short-term mortality and organ failures.