Prevalence of Underdiagnosed Fragile X Syndrome in 2 Health Systems

Society for endocrinology guideline for understanding, diagnosing and treating female hypogonadism

Female hypogonadism (FH) is a relatively common endocrine disorder in women of premenopausal age, but there are significant uncertainties and wide variation in its management. Most current guidelines are monospecialty and only address premature ovarian insufficiency (POI); some allude to management in very brief and general terms, and most rely upon the extrapolation of evidence from the studies relating to physiological estrogen deficiency in postmenopausal women. The Society for Endocrinology commissioned new guidance to provide all care providers with a multidisciplinary perspective on managing patients with all forms of FH. It has been compiled using expertise from Endocrinology, Primary Care, Gynaecology and Reproductive Health practices, with contributions from expert patients and a patient support group, to help clinicians best manage FH resulting from both POI and hypothalamo-pituitary disorders, whether organic or functional.

A holistic approach to fragile X syndrome integrated guidance for person-centred care

BACKGROUND

The Fragile X community has expressed a desire for centralised, national guidelines in the form of integrated guidance for Fragile X Syndrome (FXS).

METHODS

This article draws on existing literature reviews, primary research and clinical trials on FXS, a Fragile X Society conference workshop and first-hand experience of clinicians who have worked with those living with FXS over many years.

RESULTS

The article scopes proposed integrated guidance over the life course, including appendices of symptoms, comorbidities and referral options for FXS and Fragile X Premutation Associated Conditions.

CONCLUSION

Integrated guidance would provide an authoritative source for doctors, health professionals, therapists, care workers, social workers, educators, employers, families and those living with FXS, so that a holistic, person-centred approach can be taken across the United Kingdom to garner the best outcomes for those with FXS.

Unmethylated Mosaic Full Mutation Males without Fragile X Syndrome

Fragile X syndrome (FXS) is the leading inherited cause of intellectual disability (ID) and single gene cause of autism. Although most patients with FXS and the full mutation (FM) have complete methylation of the fragile X messenger ribonucleoprotein 1 (FMR1) gene, some have mosaicism in methylation and/or CGG repeat size, and few have completely unmethylated FM alleles. Those with a complete lack of methylation are rare, with little literature about the cognitive and behavioral phenotypes of these individuals. A review of past literature was conducted regarding individuals with unmethylated and mosaic FMR1 FM. We report three patients with an unmethylated FM FMR1 alleles without any behavioral or cognitive deficits. This is an unusual presentation for men with FM as most patients with an unmethylated FM and no behavioral phenotypes do not receive fragile X DNA testing or a diagnosis of FXS. Our cases showed that mosaic males with unmethylated FMR1 FM alleles may lack behavioral phenotypes due to the presence of smaller alleles producing the FMR1 protein (FMRP). However, these individuals could be at a higher risk of developing fragile X-associated tremor/ataxia syndrome (FXTAS) due to the increased expression of mRNA, similar to those who only have a premutation.

Advancing artificial intelligence-assisted pre-screening for fragile X syndrome

BACKGROUND

Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism, is significantly underdiagnosed in the general population. Diagnosing FXS is challenging due to the heterogeneity of the condition, subtle physical characteristics at the time of birth and similarity of phenotypes to other conditions. The medical complexity of FXS underscores an urgent need to develop more efficient and effective screening methods to identify individuals with FXS. In this study, we evaluate the effectiveness of using artificial intelligence (AI) and electronic health records (EHRs) to accelerate FXS diagnosis.

METHODS

The EHRs of 2.1 million patients served by the University of Wisconsin Health System (UW Health) were the main data source for this retrospective study. UW Health includes patients from south central Wisconsin, with approximately 33 years (1988-2021) of digitized health data. We identified all participants who received a code for FXS in the form of International Classification of Diseases (ICD), Ninth or Tenth Revision (ICD9 = 759.83, ICD10 = Q99.2). Only individuals who received the FXS code on at least two occasions ("Rule of 2") were classified as clinically diagnosed cases. To ensure the availability of sufficient data prior to clinical diagnosis to test the model, only individuals who were diagnosed after age 10 were included in the analysis. A supervised random forest classifier was used to create an AI-assisted pre-screening tool to identify cases with FXS, 5 years earlier than the time of clinical diagnosis based on their medical records. The area under receiver operating characteristic curve (AUROC) was reported. The AUROC shows the level of success in identification of cases and controls (AUROC = 1 represents perfect classification).

RESULTS

52 individuals were identified as target cases and matched with 5200 controls. AI-assisted pre-screening tool successfully identified cases with FXS, 5 years earlier than the time of clinical diagnosis with an AUROC of 0.717. A separate model trained and tested on UW Health cases achieved the AUROC of 0.798.

CONCLUSIONS

This result shows the potential utility of our tool in accelerating FXS diagnosis in real clinical settings. Earlier diagnosis can lead to more timely intervention and access to services with the goal of improving patients' health outcomes.