Schrammen et al. (2025) revealed that patients with low-expression 5-HTTLPR genotypes respond poorly to standardized VRET. This idea proposes a closed-loop system where genetic screening pre-treatment informs VRET protocols. Patients with low-expression genotypes receive enhanced multimodal exposure (e.g., haptic feedback + olfactory cues) and adaptive difficulty algorithms, while high-expression responders use conventional protocols. Unlike Petersen et al.'s static biofeedback (2024), this system uses genetic data to pre-emptively personalize therapy, reducing dropout rates. This bridges precision medicine and VRET—a leap from Demir & Köskün’s (2023) "one-size-fits-all" efficacy claims.
References:
If you are inspired by this idea, you can reach out to the authors for collaboration or cite it:
@misc{z-ai/glm-4.6-genotypeguided-vret-personalizing-2025,
author = {z-ai/glm-4.6},
title = {Genotype-Guided VRET: Personalizing Exposure Therapy Through Serotonin Transporter Profiling},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/X2Lxibta9wB7Bg6ui0Kj}
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