The role of novel COQ8B mutations in glomerulopathy and related kidney defects.

BACKGROUND AND OBJECTIVES: Glomerulopathies affect kidney glomeruli and can lead to end-stage renal disease if untreated. Clinical and experimental evidence have identified numerous (>20) genetic mutations in the mitochondrial coenzyme Q8B protein (COQ8B) primarily associated with nephrotic syndrome. Yet, little else is understood about COQ8B activity in renal pathogenesis and its role in mitochondrial dysfunction. We identified additional novel COQ8B mutations in a glomerulopathy patient and aimed to define the potential structural and functional defects of COQ8B mutations. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: Whole exome sequencing was performed on a Hispanic female presenting with proteinuria. Novel mutations in the COQ8B gene were identified. The effects of mutation on protein function, mitochondrial morphology, and disease progression were investigated by histopathology, transmission electron microscopy, homology modeling, and in silico structural analysis. RESULTS: We have characterized the pathophysiology of novel COQ8B mutations, compound heterozygous for two alterations c.1037T>G (p.I346S), and c.1560G>A (p.W520X), in the progression of proteinuria in a Hispanic female. Histopathology revealed defects in podocyte structure and mitochondrial morphology. In silico and computation analyses highlight possible structural origins of COQ8B dysfunction in the presence of mutations. CONCLUSIONS: Novel mutations in COQ8B present promising biomarkers for the early detection and therapeutic targeting of mitochondrial glomerulopathy. Insights from structural modeling suggest roles of mutation-dependent alterations in COQ8B allosteric regulation, protein folding, or stability in renal pathogenesis.

Comorbidities Among Young Adults with Congenital Heart Defects: Results from the Congenital Heart Survey To Recognize Outcomes, Needs, and well-beinG - Arizona, Arkansas, and Metropolitan Atlanta, 2016-2019.

An estimated 1.4 million adults in the United States live with congenital heart defects (CHDs), yet their health outcomes are not well understood (1). Using self-reported, cross-sectional data from 1,482 respondents in the 2016-2019 Congenital Heart Survey To Recognize Outcomes, Needs, and well-beinG (CH STRONG) (2), CDC and academic partners estimated the prevalence of comorbidities among adults with CHDs aged 20-38 years born in Arizona (AZ), Arkansas (AR), and metropolitan Atlanta, Georgia (GA) compared with the general population (aged 20-38 years) from the National Health and Nutrition Examination Survey (NHANES) during 2015-2018 (3) and the AZ, AR, and GA Behavioral Risk Factor Surveillance Systems (BRFSS) during 2016-2018 (4). Adults with CHDs were more likely than those in the general population to report cardiovascular comorbidities, such as a history of congestive heart failure (4.3% versus 0.2%) and stroke (1.4% versus 0.3%), particularly those with severe CHDs (2). Adults with CHDs were more likely to report current depressive symptoms (15.1% versus 8.5%), but less likely to report previous diagnoses of depression (14.2% versus 22.6%), asthma (12.7% versus 16.9%), or rheumatologic disease (3.2% versus 8.0%). Prevalence of noncardiovascular comorbidities was similar between adults whose CHD was considered severe and those with nonsevere CHDs. Public health practitioners and clinicians can encourage young adults with CHDs to seek appropriate medical care to help them live as healthy a life as possible.

Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application.

BACKGROUND: Electronic health records (EHRs) bring many opportunities for information utilization. One such use is the surveillance conducted by the Centers for Disease Control and Prevention to track cases of autism spectrum disorder (ASD). This process currently comprises manual collection and review of EHRs of 4- and 8-year old children in 11 US states for the presence of ASD criteria. The work is time-consuming and expensive. OBJECTIVE: Our objective was to automatically extract from EHRs the description of behaviors noted by the clinicians in evidence of the diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM). Previously, we reported on the classification of entire EHRs as ASD or not. In this work, we focus on the extraction of individual expressions of the different ASD criteria in the text. We intend to facilitate large-scale surveillance efforts for ASD and support analysis of changes over time as well as enable integration with other relevant data. METHODS: We developed a natural language processing (NLP) parser to extract expressions of 12 DSM criteria using 104 patterns and 92 lexicons (1787 terms). The parser is rule-based to enable precise extraction of the entities from the text. The entities themselves are encompassed in the EHRs as very diverse expressions of the diagnostic criteria written by different people at different times (clinicians, speech pathologists, among others). Due to the sparsity of the data, a rule-based approach is best suited until larger datasets can be generated for machine learning algorithms. RESULTS: We evaluated our rule-based parser and compared it with a machine learning baseline (decision tree). Using a test set of 6636 sentences (50 EHRs), we found that our parser achieved 76% precision, 43% recall (ie, sensitivity), and >99% specificity for criterion extraction. The performance was better for the rule-based approach than for the machine learning baseline (60% precision and 30% recall). For some individual criteria, precision was as high as 97% and recall 57%. Since precision was very high, we were assured that criteria were rarely assigned incorrectly, and our numbers presented a lower bound of their presence in EHRs. We then conducted a case study and parsed 4480 new EHRs covering 10 years of surveillance records from the Arizona Developmental Disabilities Surveillance Program. The social criteria (A1 criteria) showed the biggest change over the years. The communication criteria (A2 criteria) did not distinguish the ASD from the non-ASD records. Among behaviors and interests criteria (A3 criteria), 1 (A3b) was present with much greater frequency in the ASD than in the non-ASD EHRs. CONCLUSIONS: Our results demonstrate that NLP can support large-scale analysis useful for ASD surveillance and research. In the future, we intend to facilitate detailed analysis and integration of national datasets.

Recognition of clinical characteristics for population-based surveillance of fetal alcohol syndrome.

BACKGROUND: The diagnosis of fetal alcohol syndrome (FAS) rests on identification of characteristic facial, growth, and central nervous system (CNS) features. Public health surveillance of FAS depends on documentation of these characteristics. We evaluated if reporting of FAS characteristics is associated with the type of provider examining the child. METHODS: We analyzed cases aged 7-9 years from the Fetal Alcohol Syndrome Surveillance Network II (FASSNetII). We included cases whose surveillance records included the type of provider (qualifying provider: developmental pediatrician, geneticist, neonatologist; other physician; or other provider) who evaluated the child as well as the FAS diagnostic characteristics (facial dysmorphology, CNS impairment, and/or growth deficiency) reported by the provider. RESULTS: A total of 345 cases were eligible for this analysis. Of these, 188 (54.5%) had adequate information on type of provider. Qualifying physicians averaged more than six reported FAS characteristics while other providers averaged less than five. Qualifying physicians reported on facial characteristics and developmental delay more frequently than other providers. Also, qualifying physicians reported on all three domains of characteristics (facial, CNS, and growth) in 97% of cases while others reported all three characteristics on two thirds of cases. CONCLUSIONS: Documentation in medical records during clinical evaluations for FAS is lower than optimal for cross-provider communication and surveillance purposes. Lack of documentation limits the quality and quantity of information in records that serve as a major source of data for public health surveillance systems.