ArticleScientific reports2023
Causal effects of genetically determined blood metabolites on multiple myeloma: a Mendelian randomization study.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Lipid levels and multiple myeloma risk: insights from Meta-analysis and mendelian randomization.Lipids in health and disease · 2024Pooled it
- Development and validation of a multiple myeloma diagnostic model based on systemic lupus erythematosus-associated genes and identification of specific genes.Discover oncology · 2025Article
- Causal correlations between inflammatory proteins and heart failure: A two-sample Mendelian randomization analysis.ESC heart failure · 2025Article
- Identification of biomarkers in multiple myeloma: A comprehensive study combining microarray analysis and Mendelian randomization.Journal of cellular and molecular medicine · 2024Article
- Association between blood metabolites and basal cell carcinoma risk: a two-sample Mendelian randomization study.Frontiers in endocrinology · 2024Article
- Assessing the Genetic Causal Effects Between Blood Metabolites and Spinal Pain: A Bidirectional Two-Sample Mendelian Randomization Study.Journal of pain research · 2024Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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Abstract
Previous studies have shown that metabolites play an important role in phenotypic regulation. However, the causal relationship between metabolites and multiple myeloma has not been adequately investigated. Here, we attempt to explore the causal effects of genetically determined blood metabolites on multiple myeloma. The large-scale public blood metabolites and multiple myeloma datasets from independently published genome-wide association studies (GWAS) were used to explore the causal relationship between each genetically determined blood metabolite and multiple myeloma through inverse variance weighted (IVW), weighted median, MR-Egger and mode-based estimation methods. Sensitivity tests were performed to evaluate the stability and reliability of the results by MR-Egger regression and leave-one-out methods. Metabolic pathway analysis was further explored using filtered data. Statistical analyses were all performed in R. Among 452 metabolites, ten known metabolites and three unknown metabolites had significant causal relationship with multiple myeloma (P < 0.05). Four known metabolites, 3-methyl-2-oxovalenate, oxidized bilirubin, isovalerylcarnitine and glutamine carnitine, reached statistical significance in IVW models. Metabolic pathways analysis identified four significant pathways. The occurrence of multiple myeloma may have a causal relationship with these four metabolites, and there are four metabolic pathways that are also related to the occurrence of multiple myeloma. This can provide new ideas for exploring early screening and treatment of multiple myeloma.
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