Causality of genetically determined metabolites and metabolic pathways on osteoarthritis: a two-sample mendelian randomization study

Abstract

Background Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases and is the leading cause of pain and disability in the aged population. However, the underlying biological mechanism has not been fully understood. This study aims to reveal the causal effect of circulation metabolites on OA susceptibility. Methods A two-sample Mendelian Randomization (MR) analysis was performed to estimate the causality of GDMs on OA. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas 8 different OA phenotypes, including any-site OA (All OA), knee and/or hip OA (knee/hip OA), knee OA, hip OA, spine OA, finger and/or thumb OA (hand OA), finger OA, thumb OA, were set the outcomes. Inverse-variance weighted (IVW) was used for calculating causal estimates. Methods including weight mode, weight median, MR-egger, and MR-PRESSO were used for the sensitive analysis. Furthermore, metabolic pathway analysis was performed via the web-based Metaconflict 4.0. All statistical analyses were performed in R software. Results In this MR analysis, a total of 235 causative associations between metabolites and different OA phenotypes were observed. After false discovery rate (FDR) correction and sensitive analysis, 9 robust causative associations between 7 metabolites (e.g., arginine, kynurenine, and isovalerylcarnitine) and 5 OA phenotypes were finally identified. Additionally, eleven significant metabolic pathways in 4 OA phenotypes were identified by metabolic pathway analysis. Conclusion The finding of our study suggested that identified metabolites and metabolic pathways can be considered useful circulating metabolic biomarkers for OA screening and prevention in clinical practice, and can also serve as candidate molecules for future mechanism exploration and drug target selection.

Causal effects of gut microbiota on the prognosis of ischemic stroke: evidence from a bidirectional two-sample Mendelian randomization study

“…To address multiple comparisons (196 exposures), p -values of the IVW method have been adjusted by false discovery rate (FDR) correction. P FDR < 0.05 was considered to indicate a significant association (Xu et al, 2021 ; Gu et al, 2023 ).…”

An integrative analysis of single-cell and bulk transcriptome and bidirectional mendelian randomization analysis identified C1Q as a novel stimulated risk gene for Atherosclerosis

“…We conducted a genomewide association study (GWAS) to identify single nucleotide polymorphisms (SNPs) that were both independent and nominally associated with a significance threshold of P < 1×10-5. We used a clumping algorithm with a cutoff of r2 = 0.01 and kb = 10000 to select SNPs that were in linkage disequilibrium with each other (28,29). Previous studies have described the methods used for other Mendelian randomization (MR) analyses (30)(31)(32).…”

“…MR utilizes single nucleotide polymorphisms (SNPs) to simulate randomized controlled trials, aiming to ascertain and explore the causal association between exposure and outcome variables in epidemiological research. By effectively eliminating the confounding effects of extraneous factors and employing genetic variations as instrumental variables (IVs), MR enables the analysis of disease relationships (28)(29)(30)(31)(32).…”

Mendelian randomization and single-cell expression analyses identify the causal relationship between depression and chronic rhinosinusitis

“…Unlike observational studies, MR is a statistical approach that utilizes single nucleotide polymorphisms (SNPs), which remain independent of confounding factors due to the random allocation of alleles from parental to filial generations and the unidirectional flow from genotype to phenotype. These SNPs function as instrumental variables (IVs) to evaluate the causal relationship between an exposure and an outcome (12). Genome-wide association studies (GWAS) focused on diverse complex traits have emphasized that genetic variants are notably enriched in non-coding regions with cis-regulatory activities, and these regions are further enriched in expression quantitative trait loci (eQTL) (13).…”