Purpose: Congenital hypopituitarism (CH) disorders are phenotypically variable. Variants in multiple genes are associated with these disorders, with variable penetrance and inheritance.
Methods: We screened a large cohort (N = 1765) of patients with or at risk of CH using Sanger sequencing, selected according to phenotype, and conducted next-generation sequencing (NGS) in 51 families within our cohort. We report the clinical, hormonal, and neuroradiological phenotypes of patients with variants in known genes associated with CH.
Results: We identified variants in 178 patients: GH1/GHRHR (51 patients of 414 screened), PROP1 (17 of 253), POU1F1 (15 of 139), SOX2 (13 of 59), GLI2 (7 of 106), LHX3/LHX4 (8 of 110), HESX1 (8 of 724), SOX3 (9 of 354), OTX2 (5 of 59), SHH (2 of 64), and TCF7L1, KAL1, FGFR1, and FGF8 (2 of 585, respectively). NGS identified 26 novel variants in 35 patients (from 24 families). Magnetic resonance imaging showed prevalent hypothalamo-pituitary abnormalities, present in all patients with PROP1, GLI2, SOX3, HESX1, OTX2, LHX3, and LHX4 variants. Normal hypothalamo-pituitary anatomy was reported in 24 of 121, predominantly those with GH1, GHRHR, POU1F1, and SOX2 variants.
Conclusion: We identified variants in 10% (178 of 1765) of our CH cohort. NGS has revolutionized variant identification, and careful phenotypic patient characterization has improved our understanding of CH. We have constructed a flow chart to guide genetic analysis in these patients, which will evolve upon novel gene discoveries.
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http://dx.doi.org/10.1016/j.gim.2023.100881 | DOI Listing |
Genet Epidemiol
January 2025
Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts, USA.
Large-scale gene-environment interaction (GxE) discovery efforts often involve analytical compromises for the sake of data harmonization and statistical power. Refinement of exposures, covariates, outcomes, and population subsets may be helpful to establish often-elusive replication and evaluate potential clinical utility. Here, we used additional datasets, an expanded set of statistical models, and interrogation of lipoprotein metabolism via nuclear magnetic resonance (NMR)-based lipoprotein subfractions to refine a previously discovered GxE modifying the relationship between physical activity (PA) and HDL-cholesterol (HDL-C).
View Article and Find Full Text PDFOtolaryngol Head Neck Surg
January 2025
Department of Otolaryngology-Head and Neck Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA.
Objective: We aim to explore the differences in complication rates in endoscopic versus open transcervical treatment of Zenker diverticulum.
Study Design: Retrospective Cohort Study from January 1, 2015 to December 31, 2023.
Setting: Queries of the TriNetX database's United States Collaborative Network.
Front Cardiovasc Med
December 2024
Department of Hypertension, Henan Provincial People's Hospital, Zhengzhou, China.
Background: Previous studies suggest that frailty increases the risk of mortality, but the risk of cardiovascular disease (CVD) and all-cause mortality in Chinese community-dwelling older adults remains understudied. Our aim was to explore the effect of frailty on cardiovascular and all-cause mortality in older adults based on a large-scale prospective survey of community-dwelling older adults in China.
Methods: We utilized the 2014-2018 cohort of the Chinese Longitudinal Healthy Longevity Survey and constructed a frailty index (FI) to assess frailty status.
JAMIA Open
February 2025
Artificial Intelligence (AI) for Health Institute (AIHealth), Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Extracorporeal membrane oxygenation (ECMO) is among the most resource-intensive therapies in critical care. The COVID-19 pandemic highlighted the lack of ECMO resource allocation tools. We aimed to develop a continuous ECMO risk prediction model to enhance patient triage and resource allocation.
View Article and Find Full Text PDFBackground Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR. Methods Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.
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