Dataset Information

Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases.

ABSTRACT: We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients. Our motivating application is the Southall And Brent REvisited (SABRE) study, a tri-ethnic cohort study conducted in the UK. We are interested in identifying potential ethnic differences in metabolite levels and associations as well as their evolution over time, with the aim of gaining a better understanding of different risk of cardio-metabolic disorders across ethnicities. Within a Bayesian framework, we employ a nodewise regression approach to infer the structure of the graphs, borrowing information across time as well as across ethnicities. The response variables of interest are metabolite levels measured at two time points and for two ethnic groups, Europeans and South-Asians. We use nodewise regression to estimate the high-dimensional precision matrices of the metabolites, imposing sparsity on the regression coefficients through the dynamic horseshoe prior, thus favouring sparser graphs. We provide the code to fit the proposed model using the software Stan, which performs posterior inference using Hamiltonian Monte Carlo sampling, as well as a detailed description of a block Gibbs sampling scheme.

SUBMITTER: Molinari M

PROVIDER: S-EPMC10763914 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases.

Molinari Marco M Cremaschi Andrea A De Iorio Maria M Chaturvedi Nishi N Hughes Alun A Tillin Therese T

Journal of applied statistics 20220902 1

We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients. Our motivating application is the Southall And Brent REvisited (SABRE) study, a tri-ethnic cohort study conducted in the UK. We are interested in identifying potential ethnic differences in metabolite levels and associations as well as their evolution over time, with the aim of gaining a better understanding of differ ...[more]

PMID: 38179161

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Project description:BackgroundThe Food and Drug Administration has approved Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) inhibitors for the treatment of dyslipidemia. However, evidence of the optimal PCSK9 agents targeting PCSK9 for secondary prevention in patients with high-risk of cardiovascular events is lacking. Therefore, this study was conducted to evaluate the benefit and safety of different types of PCSK9 inhibitors.MethodsSeveral databases including Cochrane Central, Ovid Medline, and Ovid Embase were searched from inception until March 30, 2022 without language restriction. Randomized controlled trials (RCTs) comparing administration of PCSK9 inhibitors with placebo or ezetimibe for secondary prevention of cardiovascular events in patients with statin-background therapy were identified. The primary efficacy outcome was all-cause mortality. The primary safety outcome was serious adverse events.ResultsOverall, nine trials totaling 54,311 patients were identified. Three types of PCSK9 inhibitors were evaluated. The use of alirocumab was associated with reductions in all-cause mortality compared with control (RR 0.83, 95% CrI 0.72-0.95). Moreover, evolocumab was associated with increased all-cause mortality compared with alirocumab (RR 1.26, 95% CrI 1.04-1.52). We also found alirocumab was associated with decreased risk of serious adverse events (RR 0.94, 95% CrI 0.90-0.99).ConclusionsIn consideration of the fact that both PCSK9 monoclonal antibody and inclisiran enable patients to achieve recommended LDL-C target, the findings in this meta-analysis suggest that alirocumab might provide the optimal benefits regarding all-cause mortality with relatively lower SAE risks, and evolocumab might provide the optimal benefits regarding myocardial infarction for secondary prevention in patients with high-risk of cardiovascular events. Further head-to-head trials with longer follow-up and high methodologic quality are warranted to help inform subsequent guidelines for the management of these patients.

Project description:Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.

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Dataset Information

Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases.

Publications

Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases.

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