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Last Posted: Jan 13, 2025
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The Path to Genomic Screening—Far From Simple, but the Journey Has Begun

From the article: "Today’s genomic technology introduces a multitude of assays that could be deployed in health care: diagnostic testing of patients with suspected monogenic conditions, polygenic risk prediction for common diseases, pharmacogenomic analysis for drug-gene interactions, analysis of tumors for targetable somatic sequence variations, and noninvasive screening for prenatal chromosomal disorders or occult cancer. Alongside these approaches we must also grapple with screening of the ostensibly healthy population for monogenic diseases of newborns, children, and adults with either targeted or genome-scale sequencing. "

Prediction of progression to type 1 diabetes with dynamic biomarkers and risk scores

From the abstract: "Identifying biomarkers of functional ß-cell loss is an important step in the risk stratification of type 1 diabetes. Genetic risk scores (GRS), generated by profiling an array of single nucleotide polymorphisms, are a widely used type 1 diabetes risk-prediction tool. Type 1 diabetes screening studies have relied on a combination of biochemical (autoantibody) and GRS screening methodologies for identifying individuals at high-risk of type 1 diabetes. "

Generalization—a key challenge for responsible AI in patient-facing clinical applications

From the abstract: "Generalization – the ability of AI systems to apply and/or extrapolate their knowledge to new data which might differ from the original training data – is a major challenge for the effective and responsible implementation of human-centric AI applications. Current debate in bioethics proposes selective prediction as a solution. Here we explore data-based reasons for generalization challenges and look at how selective predictions might be implemented technically, focusing on clinical AI applications in real-world healthcare settings. "

Polygenic scores for longitudinal prediction of incident type 2 diabetes in an ancestrally and medically diverse primary care physician network: a patient cohort study

From the article: "Genetic information, if available, could improve T2D prediction among patients lacking measured clinical risk factors. Genome-wide association studies (GWAS) have identified hundreds of unique loci associated with T2D, the results of which can be used to calculate polygenic scores (PGS) that model genetic risk independently of established clinical risk factors including family history. Previous work has evaluated how PGS can be used within healthcare systems, but analyses have been largely cross-sectional in biobanks of mostly European ancestry, limiting the generalizability of results to a more ancestrally and medically diverse US healthcare system. "

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Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.

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