Identifying Different Immunoresistance Risk Profiles Among Experienced Aesthetic Botulinum Neurotoxin A Recipients: A Latent Class Analysis
Issued Date
2024-01-01
Resource Type
ISSN
14732130
eISSN
14732165
Scopus ID
2-s2.0-85211140189
Journal Title
Journal of Cosmetic Dermatology
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Cosmetic Dermatology (2024)
Suggested Citation
Tseng F.W., Vachiramon V., Gold M.H., Pavicic T., Tay C.M., Toh G.W., Tan D.M.K., Park J.Y. Identifying Different Immunoresistance Risk Profiles Among Experienced Aesthetic Botulinum Neurotoxin A Recipients: A Latent Class Analysis. Journal of Cosmetic Dermatology (2024). doi:10.1111/jocd.16686 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/102367
Title
Identifying Different Immunoresistance Risk Profiles Among Experienced Aesthetic Botulinum Neurotoxin A Recipients: A Latent Class Analysis
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Immunoresistance to botulinum neurotoxin A (BoNT-A) due to neutralizing antibodies (NAbs) can lead to partial or complete secondary nonresponse (SNR), potentially limiting individuals' aesthetic and/or medical therapeutic options in the short and/or long term. Understanding factors directly or indirectly influencing BoNT-A immunoresistance risk is crucial. Aims: This analysis explored patterns of latent risk factors (biological and behavioral) that may influence the risk of developing BoNT-A immunoresistance among experienced aesthetic BoNT-A recipients. Methods: Latent class analysis (LCA) was applied to survey data from 363 experienced BoNT-A recipients from six Asia-Pacific countries to identify distinct subgroups based on their patterns of risk factor or risk proxy variables. The five risk proxy variables used for modeling capture information on BoNT-A treatments (treatment indications/locations as proxies for dose), symptoms of declining efficacy, number of aesthetic treatments over the past 3 years, and clinic and BoNT-A formulation switching behaviors. These represent established risk factors and treatment-seeking behaviors suggested to influence immunoresistance risk. Results: LCA identified 3 distinct profiles of individuals, which we described based on the observed patterns of risk proxies as: “lower-risk” (55%), “moderate-risk” (39%), and “higher-risk” (6%). Individuals in the “higher-risk” profile reported higher BoNT-A exposure, more symptoms of declining efficacy, and distinct patterns of knowledge and attitudes toward BoNT-A immunoresistance that could account for their treatment-seeking behaviors. Conclusions: This study suggests that individual behaviors (the “human factor”) have a notable influence on BoNT-A immunoresistance risk. Gaining deeper insights into these factors could support more targeted and effective interventions to mitigate risk.