Large-Scale EHR-Based Phenotyping of an FMR1 Premutation Cohort at a Referral Hospital

Rare genetic diseases collectively affect hundreds of millions of people, yet their clinical features are usually defined in small and selected patient groups which do not represent the entire population. The FMR1 CGG repeat expansion, one of the most common trinucleotide disorders, illustrates what we know about a genetic carrier's susceptibility that predisposes to multiple medical problems, which unfortunately remains poorly characterised. FMR1 premutation-associated conditions (FXPAC) encompass a spectrum of clinical manifestations. FMR1 premutation carriers are at risk of developing conditions such as fragile X-associated primary ovarian insufficiency (FXPOI), fragile X-associated tremor/ataxia syndrome (FXTAS), and a range of neuropsychiatric symptoms collectively termed Fragile X-associated neuropsychiatric disorders (FXAND), which to this date remain a topic of concern and debate. Despite an estimated carrier prevalence of 1 in 151 females and 1 in 468 males, FMR1 premutation carriers frequently remain undiagnosed or misdiagnosed. Electronic Health Records (EHRs) structured in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) enable large-scale retrospective phenotyping of such rare conditions.

Using OMOP-mapped EHR data from the Mount Sinai Health System, we identified a cohort of 750 individuals with a confirmed FMR1 premutation status (98.5% female; median age at index 33.3 years), to our knowledge the largest EHR-derived FMR1 premutation cohort described within a single health system to date.

Several key FXPAC-associated diagnoses were documented before formal FMR1 premutation identification: Endocrine/Metabolic (16.5%, n = 124/750) and Neuropsychiatric (16.3%, n = 122/750) conditions were the most prevalent FXPAC domains. In every domain a substantial share of patients had their first record before the index date: the first-quartile lead time was 1.9 years for FXAND (n = 122), 1.5 years for FXPOI (n = 64), 1.0 years for both pain/fatigue (n = 19) and autoimmune/inflammatory conditions (n = 15), and 0.1 years for endocrine/metabolic conditions (n = 124); only FXTAS (n = 13) showed no pre-index lead (Q1 = 0.0 years). These lags reveal a measurable diagnostic gap prior to the identification of the FMR1 premutation. This study demonstrates that large-scale EHR-based phenotyping can characterise the clinical burden of rare genetic conditions, quantify diagnostic delays at the population scale, and identify precursor diagnosis patterns that may support earlier genetic referral in clinical practice.