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Diabetes PHGKB

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Last Posted: Mar 28, 2023
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Effectiveness of artificial intelligence screening in preventing vision loss from diabetes: a policy model.
Roomasa Channa et al. NPJ digital medicine 2023 3 (1) 53

We designed the Care Process for Preventing Vision Loss from Diabetes (CAREVL), as a Markov model to compare the effectiveness of point-of-care autonomous AI-based screening with in-office clinical exam by an eye care provider (ECP), on preventing vision loss among patients with diabetes. The estimated incidence of vision loss at 5 years was 1535 per 100,000 in the AI-screened group compared to 1625 per 100,000 in the ECP group, leading to a modelled risk difference of 90 per 100,000. The base-case CAREVL model estimated that an autonomous AI-based screening strategy would result in 27,000 fewer Americans with vision loss at 5 years compared with ECP.

Evaluation of polygenic risk scores to differentiate between type 1 and type 2 diabetes.
Muhammad Shoaib et al. Genetic epidemiology 2023 2

We evaluated PRS models for T1D and T2D in European genetic ancestry participants from the UK Biobank (UKB) and then in the Michigan Genomics Initiative (MGI). Specifically, we investigated the utility of T1D and T2D PRS to discriminate between T1D, T2D, and controls in unrelated UKB individuals of European ancestry. We derived PRS models using external non-UKB GWAS. The T1D PRS model with the best discrimination between T1D cases and controls (area under the receiver operator curve [AUC]?=?0.805) also yielded the best discrimination of T1D from T2D cases in the UKB (AUC?=?0.792) and separation in MGI (AUC?=?0.686).

Causal factors underlying diabetes risk informed by Mendelian randomisation analysis: evidence, opportunities and challenges.
Shuai Yuan et al. Diabetologia 2023 2

Diabetes and its complications cause a heavy disease burden globally. Identifying exposures, risk factors and molecular processes causally associated with the development of diabetes can provide important evidence bases for disease prevention and spur novel therapeutic strategies. Mendelian randomisation (MR), an epidemiological approach that uses genetic instruments to infer causal associations between an exposure and an outcome, can be leveraged to complement evidence from observational and clinical studies. This narrative review aims to summarize the evidence on potential causal risk factors for diabetes by integrating published MR studies on type 1 and 2 diabetes.

Beyond genetic screening-functionality-based precision medicine in monogenic obesity.
Antje Körner et al. The lancet. Diabetes & endocrinology 2023 2 (3) 143-144

Most genes causing monogenic obesity are implicated in the central energy regulatory circuits of the leptin-melanocortin pathway. Even though monogenic obesity is still a rare disease entity, identifying these patients is important since there are now promising treatment options such as setmelanotide, a melanocortin receptor agonist, which was recently approved by the US Food and Drug Administration and European Medicines Agency


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About Diabetes PHGKB

Diabetes PHGKB is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to diabetes...more

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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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