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已发表论文
血浆代谢物与皮肌炎发病风险之间的因果关系
Authors Qin W, Tian J, Qiu Y, Bao X, Yang S
Received 7 May 2025
Accepted for publication 28 September 2025
Published 13 October 2025 Volume 2025:18 Pages 2629—2643
DOI https://doi.org/10.2147/CCID.S538895
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Jeffrey Weinberg
Wenyi Qin,1 Jiayu Tian,1 Yuqin Qiu,1 Xiaorong Bao,1 Shuangshuang Yang2
1Department of Integrated Traditional Chinese and Western Medicine, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 2Department of Laboratory, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China
Correspondence: Wenyi Qin, Department of Rheumatology and Immunology/Department of Integrated Traditional Chinese and Western Medicine, the First Affiliated Hospital of Chongqing Medical University, NO. 1, Youyi Road, Yuzhong District, Chongqing, 400016, People’s Republic of China, Tel +86 18716478264, Email apple11wen@126.com
Purpose: This exploratory study aimed to investigate the potential causal relationships between plasma metabolites and dermatomyositis using Mendelian randomization (MR), with the goal of generating hypotheses for future research.
Patients and Methods: We screened well-established metabolite GWAS databases and a comprehensive dermatomyositis patient database, starting with 1,400 metabolites. Suitable instrumental variables (IVs) were selected based on genome-wide significance, LD independence (r² < 0.01), and F-statistics > 10 to minimize weak instrument bias and pleiotropy. These IVs were then integrated with dermatomyositis patient data for MR analysis, employing techniques such as inverse-variance weighting (IVW), MR-Egger regression, and weighted median approaches. Sensitivity analyses were conducted to ensure result robustness, and findings were visualized using forest plots, scatter plots, and funnel plots.
Results: The IVW method revealed 53 metabolites and metabolic ratios significantly associated with dermatomyositis. Specifically, 20 metabolites and 8 metabolic ratios were linked to a decreased risk, while 17 metabolites and 8 ratios indicated increased risk. However, none of these associations remained statistically significant after false discovery rate (FDR) correction. Notable heterogeneity was observed in Lactosyl-N-palmitoyl-sphingosine levels, and pleiotropy was evident with 3-carboxy-4-methyl-5-pentyl-2-furanpropionate (3-CMPFP). Robustness was confirmed through MR-PRESSO and leave-one-out analyses.
Conclusion: This study conducted the first exploratory Mendelian randomization analysis to investigate potential causal links between plasma metabolites and dermatomyositis. Although no statistically significant causal relationships were identified after multiple testing correction, this study provides preliminary evidence and valuable hypotheses for further research into metabolic pathways underlying dermatomyositis.
Keywords: dermatomyositis, metabolites, metabolomics, mendelian randomization, genetic association studies, inverse variance weighted, instrumental variables
1Department of Integrated Traditional Chinese and Western Medicine, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 2Department of Laboratory, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China
Correspondence: Wenyi Qin, Department of Rheumatology and Immunology/Department of Integrated Traditional Chinese and Western Medicine, the First Affiliated Hospital of Chongqing Medical University, NO. 1, Youyi Road, Yuzhong District, Chongqing, 400016, People’s Republic of China, Tel +86 18716478264, Email apple11wen@126.com
Purpose: This exploratory study aimed to investigate the potential causal relationships between plasma metabolites and dermatomyositis using Mendelian randomization (MR), with the goal of generating hypotheses for future research.
Patients and Methods: We screened well-established metabolite GWAS databases and a comprehensive dermatomyositis patient database, starting with 1,400 metabolites. Suitable instrumental variables (IVs) were selected based on genome-wide significance, LD independence (r² < 0.01), and F-statistics > 10 to minimize weak instrument bias and pleiotropy. These IVs were then integrated with dermatomyositis patient data for MR analysis, employing techniques such as inverse-variance weighting (IVW), MR-Egger regression, and weighted median approaches. Sensitivity analyses were conducted to ensure result robustness, and findings were visualized using forest plots, scatter plots, and funnel plots.
Results: The IVW method revealed 53 metabolites and metabolic ratios significantly associated with dermatomyositis. Specifically, 20 metabolites and 8 metabolic ratios were linked to a decreased risk, while 17 metabolites and 8 ratios indicated increased risk. However, none of these associations remained statistically significant after false discovery rate (FDR) correction. Notable heterogeneity was observed in Lactosyl-N-palmitoyl-sphingosine levels, and pleiotropy was evident with 3-carboxy-4-methyl-5-pentyl-2-furanpropionate (3-CMPFP). Robustness was confirmed through MR-PRESSO and leave-one-out analyses.
Conclusion: This study conducted the first exploratory Mendelian randomization analysis to investigate potential causal links between plasma metabolites and dermatomyositis. Although no statistically significant causal relationships were identified after multiple testing correction, this study provides preliminary evidence and valuable hypotheses for further research into metabolic pathways underlying dermatomyositis.
Keywords: dermatomyositis, metabolites, metabolomics, mendelian randomization, genetic association studies, inverse variance weighted, instrumental variables