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Background <p>Delayed inflammatory and fibrotic reactions to dermal fillers remain unpredictable, reflecting complex interactions between product composition and host genetics.</p> Objective <p>To develop and validate a computational framework integrating filler physicochemical attributes with simulated genetic variation to estimate relative immunologic and fibrotic risk across commercially available products.</p> Methods <p>Twenty-six fillers were analysed using a hierarchical Bayesian model combining rheologic, structural, and compositional parameters with genotype-specific modifiers in AesthetiSIM™, a reproducible Docker-based environment. Posterior distributions were derived for biostimulatory, immunogenic, and fibrotic indices, and composite scores were normalized to the [0–1] interval. Sensitivity analyses included exposure-adjusted validation, leave-one-product-out cross-validation, and variance decomposition to assess robustness.</p> Results <p>Risk scores formed a continuous spectrum. Profhilo, Juvéderm Volite, Voluma XC, and Evolysse™ SMOOTH occupied the lowest strata (mean &lt;&#xa0;0.15), whereas Sculptra, Radiesse, and HArmonyCa showed the highest (mean &gt;&#xa0;0.75). The scores quantify relative risk gradients rather than absolute event probabilities. Cluster and heatmap analyses revealed distinct mechanistic classes consistent with known material properties.</p> Conclusion <p>Dermal filler safety exists on a graded continuum determined jointly by composition and genetic susceptibility. This integrative, reproducible model provides an evidence-organized framework for personalized product selection and informed patient counselling in precision aesthetic practice.</p> Level of Evidence IV <p>This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors <a href="http://www.springer.com/00266">www.springer.com/00266</a>.</p>

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When Genes Meet Gels: Computational Immunogenetics of Dermal Fillers and Stratified Risk of Immune and Fibrotic Reactions across Compositions and Genotypes

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Eqram Rahman,
  • Parinitha Rao,
  • Alain Michon,
  • Sotirios Ioannidis,
  • Woffles T.L. Wu,
  • Jean D.A. Carruthers,
  • William Richard Webb

摘要

Background

Delayed inflammatory and fibrotic reactions to dermal fillers remain unpredictable, reflecting complex interactions between product composition and host genetics.

Objective

To develop and validate a computational framework integrating filler physicochemical attributes with simulated genetic variation to estimate relative immunologic and fibrotic risk across commercially available products.

Methods

Twenty-six fillers were analysed using a hierarchical Bayesian model combining rheologic, structural, and compositional parameters with genotype-specific modifiers in AesthetiSIM™, a reproducible Docker-based environment. Posterior distributions were derived for biostimulatory, immunogenic, and fibrotic indices, and composite scores were normalized to the [0–1] interval. Sensitivity analyses included exposure-adjusted validation, leave-one-product-out cross-validation, and variance decomposition to assess robustness.

Results

Risk scores formed a continuous spectrum. Profhilo, Juvéderm Volite, Voluma XC, and Evolysse™ SMOOTH occupied the lowest strata (mean < 0.15), whereas Sculptra, Radiesse, and HArmonyCa showed the highest (mean > 0.75). The scores quantify relative risk gradients rather than absolute event probabilities. Cluster and heatmap analyses revealed distinct mechanistic classes consistent with known material properties.

Conclusion

Dermal filler safety exists on a graded continuum determined jointly by composition and genetic susceptibility. This integrative, reproducible model provides an evidence-organized framework for personalized product selection and informed patient counselling in precision aesthetic practice.

Level of Evidence IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.