Publications by authors named "E Flowers"

Article Synopsis
  • This text addresses a correction to an academic article identified by its DOI (Digital Object Identifier): 10.3389/fendo.2024.1419812.
  • The correction is likely related to findings, data, or claims made in the original article.
  • Such corrections are important in academic publishing to ensure the accuracy and reliability of scientific information.
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Objective: The purpose of this study was to apply clustering methods to identify and characterize prediabetes phenotypes and their relationships with treatment arm and type 2 diabetes (T2D) outcomes in the Diabetes Prevention Program (DPP), and to compare the utility of additional clustering measures in phenotype characterization and T2D risk stratification.

Research Design And Methods: This was a secondary analysis of data from a subset of participants (n=994) from the previously completed Diabetes Prevention Program trial. Unsupervised k-means clustering analysis was applied to derive the optimal number of clusters of participants based on common clinical risk factors alone or common risk factors plus more comprehensive measures of glucose tolerance and body composition.

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Introduction: Advances in genomics are transforming clinical practice, but there are too few genetics professionals to meet increasing demand. Moreover, many nongeneticist clinicians feel unprepared to incorporate genetics into practice. This study assessed clinicians' unmet genomics educational needs at a single academic institution.

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Objective: Circulating microRNAs show cross-sectional associations with overweight and obesity. Few studies provided data to differentiate between a snapshot perspective on these associations versus how microRNAs characterize prodromal risk from disease pathology and complications. This study assessed longitudinal relationships between circulating microRNAs and weight at multiple time-points in the Diabetes Prevention Program trial.

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Objectives: Increasing numbers of patients experience a prolonged stay in intensive care. Yet existing quality improvement (QI) tools used to improve safety and standardize care are not designed for their specific needs. This may result in missed opportunities for care and contribute to worse outcomes.

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