Microbial Instrumental Variable Screening Links Obesity and Hair Loss Mechanisms Through Microbiome Mendelian Randomization
Keywords:
Mendelian Randomization, Gut Microbiome, Alopecia, Instrumental VariablesAbstract
The intersecting epidemics of metabolic disorders and dermatological conditions have prompted extensive investigation into systemic physiological networks, particularly the gut-skin axis. Obesity is increasingly recognized as a significant risk factor for various forms of alopecia, yet the exact causal mechanisms remain obscured by complex confounding factors inherent in observational studies. The gut microbiome has emerged as a potential mediator in this relationship, given its profound influence on systemic inflammation, endocrine regulation, and metabolic homeostasis. This paper proposes a comprehensive analytical framework utilizing microbial instrumental-variable screening within a Mendelian randomization paradigm to unearth causal links between specific gut microbial taxa, obesity-induced metabolic shifts, and subsequent hair follicle miniaturization. By employing genetic variants as instrumental variables for microbiome composition, this approach circumvents reverse causation and environmental confounding. We detail the theoretical background of the bidirectional gut-skin axis, outline stringent selection criteria for microbial instrumental variables, and discuss the analytical methodologies required to validate these causal pathways. Furthermore, we synthesize anticipated findings regarding specific microbial metabolites, such as short-chain fatty acids, and their capacity to modulate follicular immune privilege and stem cell activation. This overarching theoretical framework provides a robust foundation for future genomic and multi-omic studies aimed at identifying novel probiotic or targeted therapeutic interventions for obesity-associated hair loss.
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1. Li, Yongwei, et al. "Potential Therapeutic Targets for Androgenetic Alopecia (AGA) in Obese Individuals as Revealed by a Gut Microbiome Analysis: A Mendelian Randomization Study." Nutrients 17.11 (2025): 1892.
2. Luo, Y., Ma, H., Sun, J., Li, X., Wang, H., Qin, M., ... & Li, J. (2026). Inhibition of USAG-1 improved delayed graft function in renal transplantation. Transplant Immunology, 102348.
3. Zhao, Y., Bian, H., Mu, M., Uddin, M. R., Li, Z., Li, X., ... & Xu, M. (2024, October). Cryosam: Training-free cryoet tomogram segmentation with foundation models. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 124-134). Cham: Springer Nature Switzerland.
4. Zhang, W., Xu, Y., Zhao, F., Tarbe, M., Zhou, S., Wang, W., ... & Tang, H. (2020). The pre-existing cellular immunity to Japanese encephalitis virus heterotypically protects mice from Zika virus infection. Science Bulletin, 65(5), 402-409.
5. De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., et al. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342–1350.
6. Chu, Y. B., Li, J., Jia, P., Cui, J., Zhang, R., Kang, X., Lv, M., & Zhang, S. (2021). Irf1- and Egr1-activated transcription plays a key role in macrophage polarization: A multiomics sequencing study with partial validation. International immunopharmacology, 99, 108072. https://doi.org/10.1016/j.intimp.2021.108072
7. Yu, X., Li, J., Wang, J., Wang, Y., Li, S., Ma, X., ... & Wang, H. (2026). Prediction of risk of hearing loss by industry noise from cross-sectional and longitudinal data. Communications Medicine.
8. Ding, S., Yu, X., Wang, Q., Luo, P., Li, H., Li, Z., ... & Zhang, C. (2025). Multifunctional hydrogel sensors with dynamic covalent networks for machine learning-assisted Parkinson's disease diagnosis and encrypted human-computer interaction. Materials Today Bio, 102524.
9. Chai, L., Wang, J. M., Fan, Z. Y., Liu, Z. B., Li, X. F., & Yang, Y. (2011). Ascorbate peroxidase gene from Brassica napus enhances salt and drought tolerances in Arabidopsis thaliana. African Journal of Biotechnology, 10(79), 18085-18091.
10. Lin, Q., & Cheung, A. (2026). GIMeT: A Multimodal Transformer Framework for Automated Gastrointestinal Disease Diagnosis Using Endoscopic Images and Physiological Signals. Journal of Computer Science and Frontier Technologies, 3(3), 11-27.
11. Wang, J., Faghihpirayesh, R., Golland, P., & Gholipour, A. (2024, October). Spaer: Learning spatio-temporal equivariant representations for fetal brain motion tracking. In International Workshop on Preterm, Perinatal and Paediatric Image Analysis (pp. 3-13). Cham: Springer Nature Switzerland.
12. Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., et al. (2019). Comprehensive integration of single-cell data. Cell, 177(7), 1888–1902.
13. Shi, M., & Chow, S. C. (2024). Statistical Analysis for Rating Scale in Clinical Trials. Therapeutic Innovation & Regulatory Science, 58(5), 930-945.
14. Yang, Y., Li, G., Zhong, Y., Xu, Q., Chen, B. J., Lin, Y. T., ... & Cai, J. J. (2023). Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks. Nucleic acids research, 51(13), 6578-6592.
15. Xu, X., Bowtell, J., Fong, D. T., Young, W. R., & Williams, G. K. (2025). Kinematics of balance controls in people with chronic ankle instability during unilateral stance on a moving platform. Scientific Reports, 15(1), 1126.
16. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., et al. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.
17. Xu, X., Bowtell, J., Young, W. R., Fong, D. T., & Williams, G. K. (2025). Distinct patterns in neuromuscular adaptation to repeated perturbations in chronic ankle instability. Journal of NeuroEngineering and Rehabilitation.
18. Xu, X., Bowtell, J., Young, W. R., Fong, D. T., & Williams, G. K. (2026). Increased common corticospinal input during eyes-closed unilateral stance in people with chronic ankle instability. Scientific Reports.
19. Chai, L., Zhang, J., Fernando, W. G. D., Li, H., Huang, X., Cui, C., ... & Jiang, L. (2021). Detection of blackleg resistance gene rlm1 in double-low rapeseed accessions from Sichuan Province, by kompetitive allele-specific PCR. The Plant Pathology Journal, 37(2), 194.
20. Tao, Xiaoyu, et al. "A structural roadmap to gut health: A comprehensive review of edible insect chitin–protein complexes as alternative protein-derived, structure-specific prebiotics." International Journal of Biological Macromolecules (2026): 151312.
21. Zhang, W., Luo, Z., Gu, C., Ma, J., Cao, Y., Yuan, W., & Jin, Y. (2025). Bridging the compression-precision paradox: A hybrid architecture for clinical EEG report generation with guaranteed measurement accuracy. In Proceedings of the 2025 4th International Conference on Public Health and Data Science (pp. 201–207).
22. Yu, X., Zhang, Y., Yao, Y., Cho, W. C., & Shang, A. (2025). Biofilm-mediated immune dysregulation in chronic pulmonary diseases: mechanisms and clinical implications. Frontiers in Microbiology, 16, 1736384.
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