scholarly journals Artificial intelligence–assisted phenotype discovery of fragile X syndrome in a population-based sample

2021 ◽  
Author(s):  
Arezoo Movaghar ◽  
David Page ◽  
Danielle Scholze ◽  
Jinkuk Hong ◽  
Leann Smith DaWalt ◽  
...  
Keyword(s):  
General Population ◽  
Fragile X Syndrome ◽  
Predictive Models ◽  
Neurological Disorders ◽  
Fragile X ◽  
Population Based ◽  
Learning Approaches ◽  
Health Records ◽  
Familial Data ◽  
Wide Range

Abstract Purpose Fragile X syndrome (FXS), the most prevalent inherited cause of intellectual disability, remains underdiagnosed in the general population. Clinical studies have shown that individuals with FXS have a complex health profile leading to unique clinical needs. However, the full impact of this X-linked disorder on the health of affected individuals is unclear and the prevalence of co-occurring conditions is unknown. Methods We mined the longitudinal electronic health records from more than one million individuals to investigate the health characteristics of patients who have been clinically diagnosed with FXS. Additionally, using machine-learning approaches, we created predictive models to identify individuals with FXS in the general population. Results Our discovery-oriented approach identified the associations of FXS with a wide range of medical conditions including circulatory, endocrine, digestive, and genitourinary, in addition to mental and neurological disorders. We successfully created predictive models to identify cases five years prior to clinical diagnosis of FXS without relying on any genetic or familial data. Conclusion Although FXS is often thought of primarily as a neurological disorder, it is in fact a multisystem syndrome involving many co-occurring conditions, some primary and some secondary, and they are associated with a considerable burden on patients and their families.

scholarly journals Identification of microRNAs Associated With Human Fragile X Syndrome Using Next Generation Sequencing

2021 ◽  
Author(s):  
Maryam Sotoudeh Anvari ◽  
Hamed Vasei ◽  
Hossein Najmabadi ◽  
Reza Shervin Badv ◽  
Akram Gholipour ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Fragile X Syndrome ◽  
Deep Sequencing ◽  
Neurological Disorders ◽  
Fragile X ◽  
Expression Profiles ◽  
Fmr1 Gene ◽  
Blood Samples ◽  
Generation Sequencing ◽  
Potential Biomarkers

Abstract Fragile X syndrome (FXS) is caused by a mutation in the FMR1 gene which can lead to a loss or shortage of the FMR1 protein. This protein interacts with specific miRNAs, and a change can cause a range of neurological disorders. Therefore, miRNAs could act as a novel class of potential biomarkers for common CNS diseases. The aim of this study was to test this theory by exploring the expression profiles of various miRNAs in Iranian FXS patients using deep sequencing-based technologies, and validate the miRNAs affecting expression of the FMR1 gene. Blood samples were taken from 15 patients with FXS (9 males, 6 females) and 12 controls. 25 miRNAs were differentially expressed in individuals with FXS compared to controls. Levels of 9 miRNAs were found to be significantly changed (3 upregulated and 6 downregulated). In FXS patients, the levels of hsa-miR-532-5p, hsa-miR-652-3p and hsa-miR-4797-3p were significantly upregulated while levels of hsa-miR-191-5p, hsa-miR-181-5p, hsa-miR-26a-5p, hsa-miR-30e-5p, hsa-miR-186-5p, and hsa-miR-4797-5p exhibited significant downregulation; and these dysregulations were confirmed by RT‐qPCR. This study present altered miRNA expression in blood samples from FXS patients, which could be used for diagnostic, prognostic, and treatment purposes. Larger studies are required to confirm these preliminary results.

scholarly journals Abnormally Methylated FMR1 in Absence of a Detectable Full Mutation in a U.S.A Patient Cohort Referred for Fragile X Testing

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Charles H. Hensel ◽  
Rena J. Vanzo ◽  
Megan M. Martin ◽  
Ling Ling ◽  
Solange M. Aliaga ◽  
...  
Keyword(s):  
Dna Methylation ◽  
General Population ◽  
Fragile X Syndrome ◽  
Diagnostic Yield ◽  
Fragile X ◽  
Grey Zone ◽  
Full Mutation ◽  
Standard Testing ◽  
Sample Pooling ◽  
Positive Controls

Abstract In 2016, Methylation-Specific Quantitative Melt Analysis (MS-QMA) on 3,340 male probands increased diagnostic yield from 1.60% to 1.84% for fragile X syndrome (FXS) using a pooling approach. In this study probands from Lineagen (UT, U.S.A.) of both sexes were screened using MS-QMA without sample pooling. The cohorts included: (i) 279 probands with no FXS full mutation (FM: CGG > 200) detected by AmplideX CGG sizing; (ii) 374 negative and 47 positive controls. MS-QMA sensitivity and specificity in controls approached 100% for both sexes. For male probands with no FM detected by standard testing (n = 189), MS-QMA identified abnormal DNA methylation (mDNA) in 4% normal size (NS: < 44 CGGs), 6% grey zone (CGG 45–54) and 12% premutation (CGG 54–199) alleles. The abnormal mDNA was confirmed by AmplideX methylation sensitive (m)PCR and EpiTYPER tests. In contrast, no abnormal mDNA was detected in 89 males with NS alleles from the general population. For females, 11% of 43 probands with NS alleles by the AmplideX sizing assay had abnormal mDNA by MS-QMA, with FM / NS mosaicism confirmed by AmplideX mPCR. FMR1 MS-QMA analysis can cost-effectively screen probands of both sexes for methylation and FM mosaicism that may be missed by standard testing.

scholarly journals Development of a novel, accurate, automated, rapid, high-throughput technique suitable for population-based carrier screening for Fragile X syndrome

2007 ◽  
Vol 9 (4) ◽  
pp. 199-207 ◽  
Author(s):  
Charles M Strom ◽  
Donghui Huang ◽  
Yuanyin Li ◽  
Feras M Hantash ◽  
Jenny Rooke ◽  
...  
Keyword(s):  
Fragile X Syndrome ◽  
High Throughput ◽  
Fragile X ◽  
Population Based ◽  
Carrier Screening

Fragile X syndrome: examination of issues pertaining to population-based screening

Screening ◽  
1996 ◽  
Vol 4 (4) ◽  
pp. 175-192 ◽  
Author(s):  
Kellen L. Meadows ◽  
Stephanie L. Sherman
Keyword(s):  
Fragile X Syndrome ◽  
Fragile X ◽  
Population Based

Metformin for Treatment of Fragile X Syndrome and Other Neurological Disorders

2019 ◽  
Vol 70 (1) ◽  
pp. 167-181 ◽  
Author(s):  
Ilse Gantois ◽  
Jelena Popic ◽  
Arkady Khoutorsky ◽  
Nahum Sonenberg
Keyword(s):  
Fragile X Syndrome ◽  
Cognitive Deficits ◽  
Neurological Disorders ◽  
Fragile X ◽  
Autism Spectrum ◽  
Cellular Models ◽  
The Us ◽  
Mental Retardation Protein ◽  
Drug Treatments ◽  
Core Symptoms

Fragile X syndrome (FXS) is the most frequent inherited form of intellectual disability and autism spectrum disorder. Loss of the fragile X mental retardation protein, FMRP, engenders molecular, behavioral, and cognitive deficits in FXS patients. Experiments using different animal models advanced our knowledge of the pathophysiology of FXS and led to the discovery of many targets for drug treatments. In this review, we discuss the potential of metformin, an antidiabetic drug approved by the US Food and Drug Administration, to correct core symptoms of FXS and other neurological disorders in humans. We summarize its mechanisms of action in different animal and cellular models and human diseases.

scholarly journals Analysis of risk factor domains in psychosis patient health records

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Eben Holderness ◽  
Nicholas Miller ◽  
Philip Cawkwell ◽  
Kirsten Bolton ◽  
Marie Meteer ◽  
...  
Keyword(s):  
Risk Factor ◽  
Psychiatric Patients ◽  
Training Data ◽  
Learning Approaches ◽  
Topic Extraction ◽  
Health Records ◽  
Psychiatric Readmission ◽  
Multiword Expressions ◽  
Wide Range ◽  
Readmission Risk

Abstract Background Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health records (EHRs) span a wide range of topics and vocabulary; therefore, a psychiatric readmission prediction model must begin with a robust and interpretable topic extraction component. Results We designed and evaluated multiple multilayer perceptron and radial basis function neural networks to predict the sentences in a patient’s EHR that are associated with one or more of seven readmission risk factor domains that we identified. In contrast to our baseline cosine similarity model that is based on the methodologies of prior works, our deep learning approaches achieved considerably better F1 scores (0.83 vs 0.66) while also being more scalable and computationally efficient with large volumes of data. Additionally, we found that integrating clinically relevant multiword expressions during preprocessing improves the accuracy of our models and allows for identifying a wider scope of training data in a semi-supervised setting. Conclusion We created a data pipeline for using document vector similarity metrics to perform topic extraction on psychiatric EHR data in service of our long-term goal of creating a readmission risk classifier. We show results for our topic extraction model and identify additional features we will be incorporating in the future.

scholarly journals Language across the Lifespan in Fragile X Syndrome: Characteristics and Considerations for Assessment

2020 ◽  
Vol 10 (4) ◽  
pp. 212
Author(s):  
Anne Hoffmann ◽  
Angel Wang ◽  
Natalie Berger ◽  
Lisa Cordeiro ◽  
Rebecca Shaffer ◽  
...  
Keyword(s):  
Fragile X Syndrome ◽  
Fragile X ◽  
Language Skills ◽  
Language Assessment ◽  
Standardized Assessments ◽  
Cognitive Profiles ◽  
Strong Correlations ◽  
Very Young Children ◽  
Wide Range ◽  
Caregiver Report

While it is widely acknowledged that language development is delayed for the majority of individuals with fragile X syndrome (FXS), there has been limited research into how best to assess this area. This study aimed to deepen the understanding of standardized language assessment in FXS by addressing the three following objectives: (1) Examine the feasibility and validity of widely-used, standardized assessments in participants with FXS; (2) describe linguistic and cognitive profiles for a large sample of individuals with FXS; and (3) Compare results obtained from objective testing in clinic to those obtained using caregiver report. Results indicate that previous results indicating strong correlations between cognition and language results hold true across a wide range of ages as well as across multiple assessments, with an exception in very young children. Caregiver report tended to give lower estimates of language ability than what was found using an objectively administered assessment. Appropriate assessments remain difficult to find as a significant percentage of individuals scored at floor when scaled scores were calculated. Further, a sub-group of participants were coded for behavioral response to testing demands, the majority being able to complete a standardized assessment. These results speak to the need for assessments that provide a wider range of items so individuals can both achieve a valid score and demonstrate progress in their attainment of language skills.

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