Male-pattern hair loss (MPHL) is the most common form of hair loss in humans. Limited treatment options exist, which are not curative and vary in efficacy and invasiveness. Therapeutic and cosmetic hair growth stimulating agents that alleviate hair loss at a low risk of side effects are therefore of interest. The efficacy of hair growth-stimulating agents is mainly evaluated by hair comb tests and trichograms. These methods do not offer molecular insights, which can provide early insights into treatment response and may be useful in monitoring long-term compliance and efficacy. We propose a general concept for the molecular monitoring of hair growth stimulating agent treatment response in vivo, based on RNA and microRNA expression profiling before and during treatment. The molecular profile can be extended by individual genotype information to assess the impact of genetic constitution on treatment response. To test this methodological approach, 91 male participants with visible signs of and/or a family history of MPHL were assigned to four groups to investigate the effects of three hair growth stimulating agents versus placebo. mRNA- and microRNA-Seq was performed on plucked hair follicle samples before, after four days, and after six weeks of treatment. Genotyping was performed on DNA extracted from blood or saliva samples. Differential expression analyses identified 52 differentially expressed genes and 17 modulated pathways following treatment with the three hair growth stimulating agents. While the majority of effects were detectable after 6-week treatment, 23% of genes showed significant regulation after 4-day treatment. Integration with genetic data through pathway-based polygenic risk score analyses identified 5 associations between genetic background and treatment effects, pointing to a potential value of companion diagnostics for hair growth stimulating agents. Our data show that this molecular monitoring approach provides insights into hair growth stimulating agent treatment response as early as days within commencing treatment, and is suitable to monitor long-term treatment effects and compliance. Combined with genetic profiling, this approach may enable personalized prediction of treatment efficacy and compliance.
Copyright: © 2024 Henne et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.