Abstract Body (Do not enter title and authors here): Introduction/Background:
Danon disease (DD) is an ultra-rare X-linked disorder caused by LAMP2 protein deficiency. It frequently manifests as hypertrophic cardiomyopathy (HCM), along with skeletal myopathy and neurocognitive involvement. Due to its phenotypic variability and historically limited access to genetic testing, the condition is underdiagnosed. A recent addition of an ICD-10 code for DD (E74.05) has improved case identification, but real-world prevalence remains unknown. To address this, we developed a methodology leveraging the established epidemiology of HCM and published rates of LAMP2 mutations in genetically characterized HCM populations to estimate DD prevalence in the U.S.

Research Questions:
Can the prevalence of Danon disease be estimated using an epidemiologic model anchored in the known burden of HCM and mutation rates for LAMP2?

Methods/Approach:
We employed a modeling approach, using peer-reviewed literature to define the total HCM population in the U.S. The model integrated LAMP2 mutation frequencies derived from HCM cohorts that underwent comprehensive genetic testing. A weighted average mutation rate was calculated from these datasets and applied to the estimated U.S. HCM population.

Results/Data:
The application of literature-based LAMP2 positivity rates to the estimated HCM population produced a preliminary estimate of DD prevalence in the U.S. As this initial model does not include presymptomatic patients or those presenting with non-HCM phenotypes, such as dilated cardiomyopathy, isolated skeletal myopathy or cognitive impairment, it likely underestimates the true disease burden.

Conclusions:
This model provides a foundational methodology for estimating Danon disease prevalence in the absence of large-scale population screening. While limited to HCM-associated presentations, it underscores the importance of systematic genetic testing in unexplained cardiomyopathy and the utility of cascade testing in uncovering additional cases. Future iterations incorporating broader phenotypic criteria and real-world genetic testing data may yield more comprehensive prevalence estimates and be potentially applicable to other rare diseases.