Clinical, cosmetic and investigational dermatology · 2025
Assessing Causality Between Androgenetic Alopecia with Depression: A Bidirectional Mendelian Randomization Study.
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Li Haiyang, Cai Haibin, Li Pingwei, Zeng Yibin, Zhang Yao
Аннотация
BACKGROUND: Androgenetic alopecia (AGA) is the most common form of alopecia globally, which exerts a negative impact on patients' self-esteem and overall quality of life. Previous observational studies have found a significant increase in the prevalence of depression in AGA patients, but the causal relationship remains to be elucidated.
METHODS: In this study, we conducted a bidirectional Mendelian randomization (MR) using genome-wide association studies (GWAS) datasets. The available GWAS dataset of AGA was obtained from the Neale Lab consortium (n=154988). The dataset for depression was obtained from the ebi-a-GCST90038650 (n=484598). The main analysis method for determining the causal link between AGA and depression was inverse variance weighted (IVW). Subsequently, pleiotropy and heterogeneity tests were performed to determine the reliability of the results.
RESULTS: Utilizing the IVW method, depression does not significantly contribute to the incidence of AGA (IVW odds ratio [OR] = 1.101, 95% confidence interval [CI] =0.890-1.362, P = 0.374). Conversely, the data suggested a statistically significant association where AGA may precipitate the development of depression, with a notable increase in risk (IVW OR = 1.015, 95% CI = 1.002-1.029, P = 0.020).
CONCLUSION: We are the first to use MR analysis to explore the causal relationship between AGA and depression, revealing an increased risk of depression in individuals with AGA.
METHODS: In this study, we conducted a bidirectional Mendelian randomization (MR) using genome-wide association studies (GWAS) datasets. The available GWAS dataset of AGA was obtained from the Neale Lab consortium (n=154988). The dataset for depression was obtained from the ebi-a-GCST90038650 (n=484598). The main analysis method for determining the causal link between AGA and depression was inverse variance weighted (IVW). Subsequently, pleiotropy and heterogeneity tests were performed to determine the reliability of the results.
RESULTS: Utilizing the IVW method, depression does not significantly contribute to the incidence of AGA (IVW odds ratio [OR] = 1.101, 95% confidence interval [CI] =0.890-1.362, P = 0.374). Conversely, the data suggested a statistically significant association where AGA may precipitate the development of depression, with a notable increase in risk (IVW OR = 1.015, 95% CI = 1.002-1.029, P = 0.020).
CONCLUSION: We are the first to use MR analysis to explore the causal relationship between AGA and depression, revealing an increased risk of depression in individuals with AGA.