Exploring the Association Between Multidimensional Dietary Patterns and Non-Scarring Hair Loss Using Mendelian Randomization

利用孟德尔随机化方法探索多维饮食模式与非瘢痕性脱发之间的关联

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Abstract

BACKGROUND: Androgenetic alopecia (AGA) and alopecia areata (AA) impose significant psychosocial burdens. While pharmacological and surgical treatments exist, the role of dietary factors remains underexplored due to methodological limitations in observational studies. This Mendelian randomization (MR) study investigates causal relationships between 187 dietary exposures and hair loss, leveraging genetic variants to address confounding biases. METHODS: Genome-wide association study (GWAS) data from 161,625 UK Biobank participants were analyzed, focusing on food preferences and intake patterns. Genetic instruments for each of the 187 dietary exposures were selected at a genome-wide significance threshold (p < 5 × 10(-8)), with rigorous sensitivity analyses (MR-Egger, MR-PRESSO) to validate causality. Outcomes included AA and AGA datasets from the FinnGen consortium. RESULTS: MR analysis identified 18 specific dietary exposures significantly associated with non-scarring hair loss (FDR < 0.05). Protective effects emerged for antioxidant-rich dietary exposures, represented by higher preferences for melon, onions, and tea. Elevated risks were observed for certain exposures, including croissants, goat cheese, and whole milk. Alcohol consumption exhibited the strongest risk associations. Our extensive analysis of alcohol intake, combining data from multiple studies, consistently identified it as a significant risk factor for both alopecia areata and androgenetic alopecia. CONCLUSIONS: These findings imply modifiable dietary patterns in hair loss pathophysiology. A dual strategy is proposed: prioritizing polyphenol-rich plant foods while minimizing pro-inflammatory triggers like processed carbohydrates and alcohol. Clinically, tailored dietary adjustments-reducing ultra-processed foods and alcohol-may complement existing therapies for hair loss management.

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