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Diabetes PHGKB

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Last Posted: Nov 18, 2022
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Aiming for equitable precision medicine in diabetes.
et al. Nature medicine 2022 11 (11) 2223

New initiatives aimed at reducing the burden of diabetes are laudable, but they will have to account for the disease’s complexity and heterogeneity to be truly effective and equitable at a global scale. A growing body of evidence supports the idea that variation exists not only in disease presentation and progression but also in individual responses to therapy, which suggests that a ‘one-size-fits-all’ approach to meeting the global coverage targets will be insufficient.

Broad-capture proteomics and machine learning for early detection of type 2 diabetes risk
Nature Medicine, November 10, 2022

Impaired glucose tolerance (IGT) is a common condition that affects glucose control after sugar consumption. Isolated IGT is undetected by screening and diagnostic strategies, leaving affected individuals at high risk of developing diabetes. Here, a machine-learning framework identifies a three-protein signature for detecting isolated IGT from a single blood sample.

Proteomic signatures for identification of impaired glucose tolerance
JC Zanini et al, Nature Medicine, November 10, 2022

We applied machine learning to the proteomic profiles of a single fasted sample from 11,546 participants of the Fenland study to test discrimination of iIGT defined using the gold-standard OGTTs. We observed significantly improved discriminative performance by adding only three proteins (RTN4R, CBPM and GHR) to the best clinical model (AUROC = 0.80 (95% confidence interval: 0.79–0.86), P?=?0.004), which we validated in an external cohort. Increased plasma levels of these candidate proteins were associated with an increased risk for future T2D in an independent cohort and were also increased in individuals genetically susceptible to impaired glucose homeostasis and T2D.

Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease
I W Wu et al, NPJ Digital Medicine, November 2, 2022

Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively.


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Disclaimer: Articles listed in the Public 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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