Identifying Different Immunoresistance Risk Profiles Among Experienced Aesthetic Botulinum Neurotoxin A Recipients: A Latent Class Analysis

TL;DR

This study identifies three risk profiles for immunoresistance to botulinum toxin A among frequent users, highlighting the role of individual behaviors in influencing risk.

Contribution

The paper introduces a novel application of latent class analysis to identify distinct immunoresistance risk profiles in aesthetic BoNT-A recipients.

Findings

Three distinct immunoresistance risk profiles were identified: lower-risk (55%), moderate-risk (39%), and higher-risk (6%).

Higher-risk individuals reported greater BoNT-A exposure and more symptoms of declining efficacy.

Behavioral factors, such as treatment-seeking and knowledge, influence immunoresistance risk.

Abstract

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.

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.

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.

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.

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.

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