Evidence map›Paper›PMID 42784576›Full record

ArticlePloS one2026

Androgenetic alopecia drugs and male infertility: Evidence from pharmacovigilance and testicular transcriptomics.

Zhuozhi Gong, Jing He, Qiujian Feng, Wenyu Chen, Yonglong Xu, Qingying Wang, Shengjing Liu

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Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Zhuozhi GongDepartment of Andrology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China.ORCID https://orcid.org/0000-0002-2235-4956
Jing HeSchool of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China.
Qiujian FengBeijing University of Chinese Medicine, Beijing, China.
Wenyu ChenBeijing University of Chinese Medicine, Beijing, China.
Yonglong XuBeijing University of Chinese Medicine, Beijing, China.
Qingying WangDepartment of Dermatology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China.
Shengjing LiuDepartment of Andrology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China.ORCID https://orcid.org/0000-0003-2828-0959

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo identify disproportionality signals linking androgenetic alopecia (AGA) drugs (finasteride, dutasteride, and minoxidil) with male infertility-related adverse events and to explore infertility-related biological features using testicular transcriptomic datasets.

methodsA dual-database replication design was employed using FAERS (2004-2025) and EudraVigilance (2002-2025). Disproportionality analysis with multiple signal detection metrics assessed drug-infertility associations. Multi-level bioinformatic analyses-including toxicity prediction, drug-associated target screening, testicular transcriptomic analysis, single-cell RNA sequencing, intercellular communication analysis, gene set enrichment analysis (GSEA), and immune infiltration analysis-were integrated to explore biological features potentially relevant to male infertility.

resultsDisproportionality analyses detected signals for all three drugs, with finasteride demonstrating the most prominent reporting signal, followed by dutasteride and minoxidil. Multi-level bioinformatic analysis identified HIF1A as an overlap-derived candidate under the specified datasets and screening criteria, and HIF1A expression was higher in testicular tissue from patients with male infertility. Single-cell analysis showed higher HIF1A expression in late spermatocytes, Leydig cells, and myoid cells from infertility samples, together with group-dependent inferred communication patterns for selected VEGF-, PDGF-, IGF-, and FGF-related signaling axes. GSEA associated higher HIF1A expression with immune-response-, wound-healing-, and cell-adhesion-related processes, whereas lower HIF1A expression was associated with spermatid-development- and cilium/flagellum-dependent motility-related processes. ssGSEA-based analysis showed positive correlations between HIF1A expression and several immune-cell signature scores.

conclusionThis study systematically evaluated associations between AGA drugs and male infertility using real-world pharmacovigilance data and integrated bioinformatic analyses. The HIF1A-related transcriptomic findings provide a hypothesis-generating biological context for male infertility but do not establish a shared or drug-specific mechanism linking the three medications to infertility. The pharmacovigilance findings indicate reporting signals rather than incidence or causality.

Indexed as

AlopeciaInfertility, MaleTestisTranscriptomeComputational BiologyDutasterideFinasterideGene Expression ProfilingHumansMaleMinoxidilPharmacovigilanceDutasterideFinasterideMinoxidil

Identifiers

PMID42784576
PMCPMC13606971

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.