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The association between type 2 diabetes and asthma incidence: a longitudinal analysis considering genetic susceptibility.
BMC Public Health
January 2025
Department of Endocrinology, China-Japan Friendship Hospital, No.2 Yinghuayuan East Street, Hepingli, Chaoyang District, 100029, Beijing, China.
Background: The prevalence of type 2 diabetes (T2D) and asthma is rising, yet evidence regarding the relationship between T2D and asthma, particularly in the context of genetic predispositions, remains limited.
Methods: This study utilized data from the UK Biobank longitudinal cohort, involving 388,775 participants. A polygenic risk score (PRS) for asthma was derived from genome-wide association studies summary.
Interpreting positive celiac serology in children with new-onset type 1 diabetes.
J Pediatr Endocrinol Metab
January 2025
Division of Gastroenterology, Hepatology, & Nutrition, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Objectives: The association of celiac disease (CD) in type 1 diabetes mellitus (T1DM) is well-established, yet variation exists in screening practices. This study measures the accuracy of early screening with tissue transglutaminase Immunoglobulin A (TTG-IgA) and endomysial antibody (EMA) in newly diagnosed T1DM.
Methods: This is a retrospective study of children with T1DM between 2013 and 2019 with early CD screening and follow-up.
Resolving tissue complexity by multimodal spatial omics modeling with MISO.
Nat Methods
January 2025
Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with subcellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets.
View Article and Find Full Text PDFNew Frontiers: Umbilical Cord Mesenchymal Stem Cells Uncover Developmental Roots and Biological Underpinnings of Obesity Susceptibility.
Curr Obes Rep
January 2025
Section of Nutrition, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Purpose Of Review: To review evidence supporting human umbilical cord mesenchymal stem cells (UC-MSC) as an innovative model system advancing obesity precision medicine.
Recent Findings: Obesity prevalence is increasing rapidly and exposures during fetal development can impact individual susceptibility to obesity. UC-MSCs exhibit heterogeneous phenotypes associated with maternal exposures and predictive of child cardiometabolic outcomes.
Development of an interpretable machine learning model based on CT radiomics for the prediction of post acute pancreatitis diabetes mellitus.
Sci Rep
January 2025
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, No.63 Wenhua Road, Shunqing District, Nanchong, 637000, China.
This study sought to establish and validate an interpretable CT radiomics-based machine learning model capable of predicting post-acute pancreatitis diabetes mellitus (PPDM-A), providing clinicians with an effective predictive tool to aid patient management in a timely fashion. Clinical and imaging data from 271 patients who had undergone enhanced CT scans after first-episode acute pancreatitis from March 2017-June 2023 were retrospectively analyzed. Patients were classified into PPDM-A (n = 109) and non-PPDM-A groups (n = 162), and split into training (n = 189) and testing (n = 82) cohorts at a 7:3 ratio.
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