Human blood metabolites and risk of sepsis: A Mendelian randomization investigation

Shang, Weifeng; Qian, Hang; Zhang, Sheng; Yuan, Mingyang; Pan, Xiaojun; Huang, Sisi; Liu, Jiao; Chen, Dechang

doi:10.1111/eci.14145

Cited by 8 publications

(1 citation statement)

References 45 publications

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“…The primary statistical analysis method was the inverse variance weighted (IVW) method under random effects. This method was supplemented with weighted median analysis [23], MR-Egger regression and MR-PRESSO methods [24]. IVW assumes that all genetic variation SNPs are valid IVs with an overall bias of zero.…”

Section: Discussionmentioning

confidence: 99%

An Integrated Multi-omics Mendelian Randomization Identifies Predictive Transcription Gene Signature of Liver Fibrosis

Wang,

Zhang,

Chang

et al. 2024

Preprint

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Background Liver fibrosis is a critical deteriorating onset stage in NASH (Nonalcoholic steatohepatitis) progression towards cirrhosis and even liver cancer. Currently, there is still a lack of non-invasive diagnostic markers for hepatic fibrosis. We conduct multiple public databases associated with Pathway, Network and Mendelian randomization (MR) analysis to identify transcribed genes potentially involved in liver fibrosis and assess their diagnostic efficiency applicable to multiple races. Methods We first leveraged the advanced capabilities of the MetaIntegrator package in R. Four discovery cohorts and four validation cohorts were searched for expression profiling that biopsy diagnosed NASH patients and then the results were validated in plasma samples of two Chinese cohorts. The resulting gene signature was then conducted by GO enrichment analysis and DisGeNET enrichment analysis. Network analysis were employed using MetaboAnalyst 5.0. We then conducted MR analysis using data from IEU Open GWAS project (average N = 23,818), and GWAS Catalog (N = 8,299), the UK Biobank (N = 3,108) and FinnGen (average N = 373,007). Results Through the primary analysis of the eight cohorts and subsequent validation in Chinese cohorts, we identified a 25-gene signature that can predict NASH and liver fibrosis with a high accuracy (ROC ≥ 0.87). Pathway, network and MR analysis revealed 21 metabolites and 12 genes have causal associations with NASH/liver fibrosis. And eventually a 12-gene signature predictive (ROC ≥ 0.75) were validated as a valuable tool for distinguishing Chinese patients with liver fibrosis from those with normal NAFLD or NASH. Conclusions This study developed a 12-gene signature for predicting liver fibrosis, demonstrating the utility of an integrated an integrated genome-metabolome-Mendelian Randomization approach for predicting disease progression across various databases.

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Section: Discussionmentioning

confidence: 99%