| Title: | RETRACTED: The causal relationship between androgenetic alopecia and serum uric acid: A bidirectional Mendelian randomization analysis |
| Journal: | Skin Research and Technology |
| Published: | 14 Aug 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39140305/ |
| DOI: | https://doi.org/10.1111/srt.13912 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/11322824 |
Publication 13131
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Abstract
BACKGROUND: Despite numerous studies investigating the association between androgenetic alopecia (AGA) and serum uric acid (SUA), the causal relationship between AGA and SUA remains unknown.</p>
METHODS: We utilized bidirectional Mendelian randomization (MR) to explore the causality between AGA and SUA. Our study chose single nucleotide polymorphisms associated with genome-wide significance (p < 5×10-8) for the exposure and showing low linkage disequilibrium (R2 < 0.001) as IVs. Various MR methods were employed to evaluate causality, including inverse-variance weighted (IVW), Weighted Median, MR-Egger, Weighted Mode and Simple Mode. Sensitivity analyses were performed to test the robustness of the results.</p>
RESULTS: Using the IVW method, we did not find a significant causal relationship between AGA and SUA (OR = 1.00, 95% CI 0.99-1.01; p = 0.451). Similarly, the IVW method did not reveal evidence of causality between SUA and AGA (OR = 0.97, 95% CI = 0.91-1.03; p = 0.301). The results from other methods were consistent with those of the IVW approach.</p>
CONCLUSION: The study did not identify a causal relationship between AGA and SUA. Future research should involve larger cohorts and advanced methods to validate the findings and explore the complex interactions between AGA and SUA levels in different populations.</p>
9 Keywords
- Alopecia
- Causality
- Female
- Genome-Wide Association Study
- Humans
- Male
- Mendelian Randomization Analysis
- Polymorphism, Single Nucleotide
- Uric Acid
5 Authors
- Yan Yu
- Shu Wang
- Zijun Wu
- Jinru Lin
- Huiping Wang
1 Application
| Application ID | Title |
|---|---|
| 30880 | Big data on brain images |
Enabling scientific discoveries that improve human health