Assessing the influence of plasma metabolites on chronic skin ulcer risk: a two-sample Mendelian randomization study

Chronic skin ulcers, although rare, pose severe and debilitating challenges. The identification of causal metabolite biomarkers presents an opportunity to refine effective risk assessment strategies for this condition. In this study, we conducted a comprehensive Two-Sample Mendelian Randomization (TSMR) investigation to delineate the potential causal effects of plasma metabolites on chronic skin ulcer risk. Exposure data comprised 14,296 participants with 913 metabolites from INTERVAL/EPIC-Norfolk, and 8,299 participants with 1,091 metabolites and 309 ratios from the Canadian Longitudinal Study on Aging (CLSA). Outcome data came from the finngen_R9_L12_CHRONICULCEROFSKIN (1,840 cases, 353,088 controls) and UK Biobank Chronic ulcer of skin (495 cases, 455,853 controls) cohorts. Leveraging the inverse-variance weighted (IVW) method, alongside MR-Egger and MR-PRESSO sensitivity analyses, we evaluated metabolite associations with chronic skin ulcer risk. Further assessment involved a phenome-wide MR (Phe-MR) analysis to explore potential repercussions of targeting identified metabolites for intervention. Our study identified 12 distinct metabolites significantly associated with chronic skin ulcers, demonstrating consistent and replicable results. Notably, X-19,141 exhibited the highest reproducibility. These findings highlight novel plasma metabolites relevant to chronic skin ulcers, offering theoretical underpinnings for mechanistic research and clinical strategies in prevention and treatment.

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Publication Details

Journal
Scientific Reports
Published
2025-03-22
DOI
https://doi.org/10.1038/s41598-025-94311-8
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00

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article

Assessing the influence of plasma metabolites on chronic skin ulcer risk: a two-sample Mendelian randomization study

Ye Zheng, Wenbin Liu, Peng Xu, Jiaqi Yuan et al.
1 links
Scientific Reports
Genetic Associations and Epidemiology
article

Assessing the influence of plasma metabolites on chronic skin ulcer risk: a two-sample Mendelian randomization study

Ye Zheng, Wenbin Liu, Peng Xu, Jiaqi Yuan, Deqing Hong
article en

Abstract

Chronic skin ulcers, although rare, pose severe and debilitating challenges. The identification of causal metabolite biomarkers presents an opportunity to refine effective risk assessment strategies for this condition. In this study, we conducted a comprehensive Two-Sample Mendelian Randomization (TSMR) investigation to delineate the potential causal effects of plasma metabolites on chronic skin ulcer risk. Exposure data comprised 14,296 participants with 913 metabolites from INTERVAL/EPIC-Norfolk, and 8,299 participants with 1,091 metabolites and 309 ratios from the Canadian Longitudinal Study on Aging (CLSA). Outcome data came from the finngen_R9_L12_CHRONICULCEROFSKIN (1,840 cases, 353,088 controls) and UK Biobank Chronic ulcer of skin (495 cases, 455,853 controls) cohorts. Leveraging the inverse-variance weighted (IVW) method, alongside MR-Egger and MR-PRESSO sensitivity analyses, we evaluated metabolite associations with chronic skin ulcer risk. Further assessment involved a phenome-wide MR (Phe-MR) analysis to explore potential repercussions of targeting identified metabolites for intervention. Our study identified 12 distinct metabolites significantly associated with chronic skin ulcers, demonstrating consistent and replicable results. Notably, X-19,141 exhibited the highest reproducibility. These findings highlight novel plasma metabolites relevant to chronic skin ulcers, offering theoretical underpinnings for mechanistic research and clinical strategies in prevention and treatment.

Scientific ReportsVol. 15(1)
Guangzhou University (CN), Qiannan Normal College For Nationalities (CN)
National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province, Guangzhou Municipal Science and Technology Bureau
Zero hunger
Openalex Percentile: Top 93%
Genetic Associations and Epidemiology
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